{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c4a2457e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch            : 1.10.1\n",
      "pytorch_lightning: 1.5.1\n",
      "torchmetrics     : 0.6.2\n",
      "matplotlib       : 3.3.4\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55c28ce9",
   "metadata": {},
   "source": [
    "The three extensions below are optional, for more information, see\n",
    "- `watermark`:  https://github.com/rasbt/watermark\n",
    "- `pycodestyle_magic`: https://github.com/mattijn/pycodestyle_magic\n",
    "- `nb_black`: https://github.com/dnanhkhoa/nb_black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6a80a3d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext pycodestyle_magic\n",
    "%flake8_on --ignore W291,W293,E703,E402,E999 --max_line_length=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b7e13545",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"%load_ext nb_black\";\n",
       "                var nbb_formatted_code = \"%load_ext nb_black\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext nb_black"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e18ceb5a",
   "metadata": {},
   "source": [
    "<a href=\"https://pytorch.org\"><img src=\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\" width=\"90\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\"https://www.pytorchlightning.ai\"><img src=\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\" width=\"150\"/></a>\n",
    "\n",
    "# MobileNetV3 (Small) Trained on CIFAR-10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b0d4018",
   "metadata": {},
   "source": [
    "MobileNetV3 [1] trained on CIFAR-10 [2].\n",
    "\n",
    "### References\n",
    "\n",
    "- [1] Searching for MobileNetV3, https://arxiv.org/abs/1905.02244.pdf\n",
    "- [2] https://en.wikipedia.org/wiki/CIFAR-10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0254dd9a",
   "metadata": {},
   "source": [
    "## General settings and hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84304d17",
   "metadata": {},
   "source": [
    "- Here, we specify some general hyperparameter values and general settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aa33e94f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"BATCH_SIZE = 256\\nNUM_EPOCHS = 150\\nLEARNING_RATE = 0.001\\nNUM_WORKERS = 4\";\n",
       "                var nbb_formatted_code = \"BATCH_SIZE = 256\\nNUM_EPOCHS = 150\\nLEARNING_RATE = 0.001\\nNUM_WORKERS = 4\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "BATCH_SIZE = 256\n",
    "NUM_EPOCHS = 150\n",
    "LEARNING_RATE = 0.001\n",
    "NUM_WORKERS = 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95a361d7",
   "metadata": {},
   "source": [
    "- Note that using multiple workers can sometimes cause issues with too many open files in PyTorch for small datasets. If we have problems with the data loader later, try setting `NUM_WORKERS = 0` and reload the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc5b39d",
   "metadata": {},
   "source": [
    "## Implementing a Neural Network using PyTorch Lightning's `LightningModule`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "225f3881",
   "metadata": {},
   "source": [
    "- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\n",
    "- In essence, `LightningModule` is a wrapper around a PyTorch module.\n",
    "- We start with defining our neural network model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\n",
    "- Here, for the PyTorch model, we are using an implementation from the Torchvision hub:\n",
    "\n",
    "In this case, since Torchvision already offers a nice and efficient PyTorch implementation of MobileNet-v2, let's load it from the Torchvision hub:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cccb0db9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in /home/jovyan/.cache/torch/hub/pytorch_vision_v0.11.0\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"import torch\\n\\n\\npytorch_model = torch.hub.load(\\n    'pytorch/vision:v0.11.0',\\n    'mobilenet_v3_small',\\n    pretrained=False)\";\n",
       "                var nbb_formatted_code = \"import torch\\n\\n\\npytorch_model = torch.hub.load(\\n    \\\"pytorch/vision:v0.11.0\\\", \\\"mobilenet_v3_small\\\", pretrained=False\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
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       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "\n",
    "pytorch_model = torch.hub.load(\n",
    "    'pytorch/vision:v0.11.0',\n",
    "    'mobilenet_v3_small',\n",
    "    pretrained=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31b672eb",
   "metadata": {},
   "source": [
    "- Since the Torchvision model above was implemented for ImageNet, which has a different number of classes than CIFAR-10, we define our own output layer below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b018ec1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"pytorch_model.classifier[-1] = torch.nn.Linear(\\n        in_features=1024,  # as in the original output layer\\n        out_features=10)  # number of class labels in CIFAR-10)\";\n",
       "                var nbb_formatted_code = \"pytorch_model.classifier[-1] = torch.nn.Linear(\\n    in_features=1024, out_features=10  # as in the original output layer\\n)  # number of class labels in CIFAR-10)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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   "source": [
    "pytorch_model.classifier[-1] = torch.nn.Linear(\n",
    "        in_features=1024,  # as in the original output layer\n",
    "        out_features=10)  # number of class labels in CIFAR-10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78d64895",
   "metadata": {},
   "source": [
    "- Next, we can define our LightningModule as a wrapper around our PyTorch model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a392671b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\nimport pytorch_lightning as pl\\nimport torchmetrics\\n\\n\\n# LightningModule that receives a PyTorch model as input\\nclass LightningModel(pl.LightningModule):\\n    def __init__(self, model, learning_rate):\\n        super().__init__()\\n\\n        self.learning_rate = learning_rate\\n        # The inherited PyTorch module\\n        self.model = model\\n        if hasattr(model, \\\"dropout_proba\\\"):\\n            self.dropout_proba = model.dropout_proba\\n\\n        # Save settings and hyperparameters to the log directory\\n        # but skip the model parameters\\n        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\\n        # Set up attributes for computing the accuracy\\n        self.train_acc = torchmetrics.Accuracy()\\n        self.valid_acc = torchmetrics.Accuracy()\\n        self.test_acc = torchmetrics.Accuracy()\\n\\n    # Defining the forward method is only necessary\\n    # if you want to use a Trainer's .predict() method (optional)\\n    def forward(self, x):\\n        return self.model(x)\\n\\n    # A common forward step to compute the loss and labels\\n    # this is used for training, validation, and testing below\\n    def _shared_step(self, batch):\\n        features, true_labels = batch\\n        logits = self(features)\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n        predicted_labels = torch.argmax(logits, dim=1)\\n\\n        return loss, true_labels, predicted_labels\\n\\n    def training_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.log(\\\"train_loss\\\", loss)\\n\\n        # Do another forward pass in .eval() mode to compute accuracy\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\n        # during evaluation (inference)\\n        self.model.eval()\\n        with torch.no_grad():\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\n        self.train_acc(predicted_labels, true_labels)\\n        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n        self.model.train()\\n\\n        return loss  # this is passed to the optimzer for training\\n\\n    def validation_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.log(\\\"valid_loss\\\", loss)\\n        self.valid_acc(predicted_labels, true_labels)\\n        self.log(\\n            \\\"valid_acc\\\",\\n            self.valid_acc,\\n            on_epoch=True,\\n            on_step=False,\\n            prog_bar=True,\\n        )\\n\\n    def test_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.test_acc(predicted_labels, true_labels)\\n        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\\n    def configure_optimizers(self):\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n        return optimizer\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\nimport pytorch_lightning as pl\\nimport torchmetrics\\n\\n\\n# LightningModule that receives a PyTorch model as input\\nclass LightningModel(pl.LightningModule):\\n    def __init__(self, model, learning_rate):\\n        super().__init__()\\n\\n        self.learning_rate = learning_rate\\n        # The inherited PyTorch module\\n        self.model = model\\n        if hasattr(model, \\\"dropout_proba\\\"):\\n            self.dropout_proba = model.dropout_proba\\n\\n        # Save settings and hyperparameters to the log directory\\n        # but skip the model parameters\\n        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\\n        # Set up attributes for computing the accuracy\\n        self.train_acc = torchmetrics.Accuracy()\\n        self.valid_acc = torchmetrics.Accuracy()\\n        self.test_acc = torchmetrics.Accuracy()\\n\\n    # Defining the forward method is only necessary\\n    # if you want to use a Trainer's .predict() method (optional)\\n    def forward(self, x):\\n        return self.model(x)\\n\\n    # A common forward step to compute the loss and labels\\n    # this is used for training, validation, and testing below\\n    def _shared_step(self, batch):\\n        features, true_labels = batch\\n        logits = self(features)\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n        predicted_labels = torch.argmax(logits, dim=1)\\n\\n        return loss, true_labels, predicted_labels\\n\\n    def training_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.log(\\\"train_loss\\\", loss)\\n\\n        # Do another forward pass in .eval() mode to compute accuracy\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\n        # during evaluation (inference)\\n        self.model.eval()\\n        with torch.no_grad():\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\n        self.train_acc(predicted_labels, true_labels)\\n        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n        self.model.train()\\n\\n        return loss  # this is passed to the optimzer for training\\n\\n    def validation_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.log(\\\"valid_loss\\\", loss)\\n        self.valid_acc(predicted_labels, true_labels)\\n        self.log(\\n            \\\"valid_acc\\\",\\n            self.valid_acc,\\n            on_epoch=True,\\n            on_step=False,\\n            prog_bar=True,\\n        )\\n\\n    def test_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.test_acc(predicted_labels, true_labels)\\n        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\\n    def configure_optimizers(self):\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n        return optimizer\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\n",
    "import pytorch_lightning as pl\n",
    "import torchmetrics\n",
    "\n",
    "\n",
    "# LightningModule that receives a PyTorch model as input\n",
    "class LightningModel(pl.LightningModule):\n",
    "    def __init__(self, model, learning_rate):\n",
    "        super().__init__()\n",
    "\n",
    "        self.learning_rate = learning_rate\n",
    "        # The inherited PyTorch module\n",
    "        self.model = model\n",
    "        if hasattr(model, \"dropout_proba\"):\n",
    "            self.dropout_proba = model.dropout_proba\n",
    "\n",
    "        # Save settings and hyperparameters to the log directory\n",
    "        # but skip the model parameters\n",
    "        self.save_hyperparameters(ignore=[\"model\"])\n",
    "\n",
    "        # Set up attributes for computing the accuracy\n",
    "        self.train_acc = torchmetrics.Accuracy()\n",
    "        self.valid_acc = torchmetrics.Accuracy()\n",
    "        self.test_acc = torchmetrics.Accuracy()\n",
    "\n",
    "    # Defining the forward method is only necessary\n",
    "    # if you want to use a Trainer's .predict() method (optional)\n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "    # A common forward step to compute the loss and labels\n",
    "    # this is used for training, validation, and testing below\n",
    "    def _shared_step(self, batch):\n",
    "        features, true_labels = batch\n",
    "        logits = self(features)\n",
    "        loss = torch.nn.functional.cross_entropy(logits, true_labels)\n",
    "        predicted_labels = torch.argmax(logits, dim=1)\n",
    "\n",
    "        return loss, true_labels, predicted_labels\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.log(\"train_loss\", loss)\n",
    "\n",
    "        # Do another forward pass in .eval() mode to compute accuracy\n",
    "        # while accountingfor Dropout, BatchNorm etc. behavior\n",
    "        # during evaluation (inference)\n",
    "        self.model.eval()\n",
    "        with torch.no_grad():\n",
    "            _, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.train_acc(predicted_labels, true_labels)\n",
    "        self.log(\"train_acc\", self.train_acc, on_epoch=True, on_step=False)\n",
    "        self.model.train()\n",
    "\n",
    "        return loss  # this is passed to the optimzer for training\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.log(\"valid_loss\", loss)\n",
    "        self.valid_acc(predicted_labels, true_labels)\n",
    "        self.log(\n",
    "            \"valid_acc\",\n",
    "            self.valid_acc,\n",
    "            on_epoch=True,\n",
    "            on_step=False,\n",
    "            prog_bar=True,\n",
    "        )\n",
    "\n",
    "    def test_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.test_acc(predicted_labels, true_labels)\n",
    "        self.log(\"test_acc\", self.test_acc, on_epoch=True, on_step=False)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n",
    "        return optimizer\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d82de0f",
   "metadata": {},
   "source": [
    "## Setting up the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63ff1a40",
   "metadata": {},
   "source": [
    "- In this section, we are going to set up our dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "416dfb19",
   "metadata": {},
   "source": [
    "### Inspecting the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4b0d68f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "\n",
      "Training label distribution:\n",
      "\n",
      "Test label distribution:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(0, 1000),\n",
       " (1, 1000),\n",
       " (2, 1000),\n",
       " (3, 1000),\n",
       " (4, 1000),\n",
       " (5, 1000),\n",
       " (6, 1000),\n",
       " (7, 1000),\n",
       " (8, 1000),\n",
       " (9, 1000)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 8;\n",
       "                var nbb_unformatted_code = \"# %load ../code_dataset/dataset_cifar10_check.py\\nfrom collections import Counter\\nfrom torchvision import datasets\\nfrom torchvision import transforms\\nfrom torch.utils.data import DataLoader\\n\\n\\ntrain_dataset = datasets.CIFAR10(\\n    root=\\\"./data\\\", train=True, transform=transforms.ToTensor(), download=True\\n)\\n\\ntrain_loader = DataLoader(\\n    dataset=train_dataset,\\n    batch_size=BATCH_SIZE,\\n    num_workers=NUM_WORKERS,\\n    drop_last=True,\\n    shuffle=True,\\n)\\n\\ntest_dataset = datasets.CIFAR10(\\n    root=\\\"./data\\\", train=False, transform=transforms.ToTensor()\\n)\\n\\ntest_loader = DataLoader(\\n    dataset=test_dataset,\\n    batch_size=BATCH_SIZE,\\n    num_workers=NUM_WORKERS,\\n    drop_last=False,\\n    shuffle=False,\\n)\\n\\ntrain_counter = Counter()\\nfor images, labels in train_loader:\\n    train_counter.update(labels.tolist())\\n\\ntest_counter = Counter()\\nfor images, labels in test_loader:\\n    test_counter.update(labels.tolist())\\n\\nprint(\\\"\\\\nTraining label distribution:\\\")\\nsorted(train_counter.items())\\n\\nprint(\\\"\\\\nTest label distribution:\\\")\\nsorted(test_counter.items())\";\n",
       "                var nbb_formatted_code = \"# %load ../code_dataset/dataset_cifar10_check.py\\nfrom collections import Counter\\nfrom torchvision import datasets\\nfrom torchvision import transforms\\nfrom torch.utils.data import DataLoader\\n\\n\\ntrain_dataset = datasets.CIFAR10(\\n    root=\\\"./data\\\", train=True, transform=transforms.ToTensor(), download=True\\n)\\n\\ntrain_loader = DataLoader(\\n    dataset=train_dataset,\\n    batch_size=BATCH_SIZE,\\n    num_workers=NUM_WORKERS,\\n    drop_last=True,\\n    shuffle=True,\\n)\\n\\ntest_dataset = datasets.CIFAR10(\\n    root=\\\"./data\\\", train=False, transform=transforms.ToTensor()\\n)\\n\\ntest_loader = DataLoader(\\n    dataset=test_dataset,\\n    batch_size=BATCH_SIZE,\\n    num_workers=NUM_WORKERS,\\n    drop_last=False,\\n    shuffle=False,\\n)\\n\\ntrain_counter = Counter()\\nfor images, labels in train_loader:\\n    train_counter.update(labels.tolist())\\n\\ntest_counter = Counter()\\nfor images, labels in test_loader:\\n    test_counter.update(labels.tolist())\\n\\nprint(\\\"\\\\nTraining label distribution:\\\")\\nsorted(train_counter.items())\\n\\nprint(\\\"\\\\nTest label distribution:\\\")\\nsorted(test_counter.items())\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_dataset/dataset_cifar10_check.py\n",
    "from collections import Counter\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "\n",
    "train_dataset = datasets.CIFAR10(\n",
    "    root=\"./data\", train=True, transform=transforms.ToTensor(), download=True\n",
    ")\n",
    "\n",
    "train_loader = DataLoader(\n",
    "    dataset=train_dataset,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    num_workers=NUM_WORKERS,\n",
    "    drop_last=True,\n",
    "    shuffle=True,\n",
    ")\n",
    "\n",
    "test_dataset = datasets.CIFAR10(\n",
    "    root=\"./data\", train=False, transform=transforms.ToTensor()\n",
    ")\n",
    "\n",
    "test_loader = DataLoader(\n",
    "    dataset=test_dataset,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    num_workers=NUM_WORKERS,\n",
    "    drop_last=False,\n",
    "    shuffle=False,\n",
    ")\n",
    "\n",
    "train_counter = Counter()\n",
    "for images, labels in train_loader:\n",
    "    train_counter.update(labels.tolist())\n",
    "\n",
    "test_counter = Counter()\n",
    "for images, labels in test_loader:\n",
    "    test_counter.update(labels.tolist())\n",
    "\n",
    "print(\"\\nTraining label distribution:\")\n",
    "sorted(train_counter.items())\n",
    "\n",
    "print(\"\\nTest label distribution:\")\n",
    "sorted(test_counter.items())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5496d9c6",
   "metadata": {},
   "source": [
    "### Performance baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "028122aa",
   "metadata": {},
   "source": [
    "- Especially for imbalanced datasets, it's pretty helpful to compute a performance baseline.\n",
    "- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- we want our model to be better than that!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "043b6e24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Majority class: 3\n",
      "Accuracy when always predicting the majority class:\n",
      "0.10 (10.00%)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"# %load ../code_dataset/performance_baseline.py\\nmajority_class = test_counter.most_common(1)[0]\\nprint(\\\"Majority class:\\\", majority_class[0])\\n\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\nprint(\\\"Accuracy when always predicting the majority class:\\\")\\nprint(f\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\")\";\n",
       "                var nbb_formatted_code = \"# %load ../code_dataset/performance_baseline.py\\nmajority_class = test_counter.most_common(1)[0]\\nprint(\\\"Majority class:\\\", majority_class[0])\\n\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\nprint(\\\"Accuracy when always predicting the majority class:\\\")\\nprint(f\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_dataset/performance_baseline.py\n",
    "majority_class = test_counter.most_common(1)[0]\n",
    "print(\"Majority class:\", majority_class[0])\n",
    "\n",
    "baseline_acc = majority_class[1] / sum(test_counter.values())\n",
    "print(\"Accuracy when always predicting the majority class:\")\n",
    "print(f\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75bfc480",
   "metadata": {},
   "source": [
    "## A quick visual check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e152605d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"# %load ../code_dataset/plot_visual-check_basic.py\\n%matplotlib inline\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\nimport torchvision\\n\\n\\nfor images, labels in train_loader:  \\n    break\\n\\nplt.figure(figsize=(8, 8))\\nplt.axis(\\\"off\\\")\\nplt.title(\\\"Training images\\\")\\nplt.imshow(np.transpose(torchvision.utils.make_grid(\\n    images[:64], \\n    padding=2,\\n    normalize=True),\\n    (1, 2, 0)))\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# %load ../code_dataset/plot_visual-check_basic.py\\n%matplotlib inline\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\nimport torchvision\\n\\n\\nfor images, labels in train_loader:\\n    break\\n\\nplt.figure(figsize=(8, 8))\\nplt.axis(\\\"off\\\")\\nplt.title(\\\"Training images\\\")\\nplt.imshow(\\n    np.transpose(\\n        torchvision.utils.make_grid(images[:64], padding=2, normalize=True), (1, 2, 0)\\n    )\\n)\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_dataset/plot_visual-check_basic.py\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torchvision\n",
    "\n",
    "\n",
    "for images, labels in train_loader:  \n",
    "    break\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Training images\")\n",
    "plt.imshow(np.transpose(torchvision.utils.make_grid(\n",
    "    images[:64], \n",
    "    padding=2,\n",
    "    normalize=True),\n",
    "    (1, 2, 0)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f190981",
   "metadata": {},
   "source": [
    "### Setting up a `DataModule`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a305d2f",
   "metadata": {},
   "source": [
    "- There are three main ways we can prepare the dataset for Lightning. We can\n",
    "  1. make the dataset part of the model;\n",
    "  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the following subsection;\n",
    "  3. create a LightningDataModule.\n",
    "- Here, we will use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods, as we can see below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7ec3fd18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 11;\n",
       "                var nbb_unformatted_code = \"import os\\n\\nfrom torch.utils.data.dataset import random_split\\nfrom torch.utils.data import DataLoader\\nfrom torchvision import transforms\\n\\n\\nclass DataModule(pl.LightningDataModule):\\n    def __init__(self, data_path=\\\"./\\\"):\\n        super().__init__()\\n        self.data_path = data_path\\n\\n    def prepare_data(self):\\n        datasets.CIFAR10(root=self.data_path, download=True)\\n\\n        self.train_transform = transforms.Compose(\\n            [\\n                transforms.Resize((70, 70)),\\n                transforms.RandomCrop((64, 64)),\\n                transforms.ToTensor(),\\n            ]\\n        )\\n\\n        self.test_transform = transforms.Compose(\\n            [\\n                transforms.Resize((70, 70)),\\n                transforms.CenterCrop((64, 64)),\\n                transforms.ToTensor(),\\n            ]\\n        )\\n        return\\n\\n    def setup(self, stage=None):\\n        train = datasets.CIFAR10(\\n            root=self.data_path,\\n            train=True,\\n            transform=self.train_transform,\\n            download=False,\\n        )\\n\\n        self.test = datasets.CIFAR10(\\n            root=self.data_path,\\n            train=False,\\n            transform=self.test_transform,\\n            download=False,\\n        )\\n\\n        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\n\\n    def train_dataloader(self):\\n        train_loader = DataLoader(\\n            dataset=self.train,\\n            batch_size=BATCH_SIZE,\\n            drop_last=True,\\n            shuffle=True,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return train_loader\\n\\n    def val_dataloader(self):\\n        valid_loader = DataLoader(\\n            dataset=self.valid,\\n            batch_size=BATCH_SIZE,\\n            drop_last=False,\\n            shuffle=False,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return valid_loader\\n\\n    def test_dataloader(self):\\n        test_loader = DataLoader(\\n            dataset=self.test,\\n            batch_size=BATCH_SIZE,\\n            drop_last=False,\\n            shuffle=False,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return test_loader\";\n",
       "                var nbb_formatted_code = \"import os\\n\\nfrom torch.utils.data.dataset import random_split\\nfrom torch.utils.data import DataLoader\\nfrom torchvision import transforms\\n\\n\\nclass DataModule(pl.LightningDataModule):\\n    def __init__(self, data_path=\\\"./\\\"):\\n        super().__init__()\\n        self.data_path = data_path\\n\\n    def prepare_data(self):\\n        datasets.CIFAR10(root=self.data_path, download=True)\\n\\n        self.train_transform = transforms.Compose(\\n            [\\n                transforms.Resize((70, 70)),\\n                transforms.RandomCrop((64, 64)),\\n                transforms.ToTensor(),\\n            ]\\n        )\\n\\n        self.test_transform = transforms.Compose(\\n            [\\n                transforms.Resize((70, 70)),\\n                transforms.CenterCrop((64, 64)),\\n                transforms.ToTensor(),\\n            ]\\n        )\\n        return\\n\\n    def setup(self, stage=None):\\n        train = datasets.CIFAR10(\\n            root=self.data_path,\\n            train=True,\\n            transform=self.train_transform,\\n            download=False,\\n        )\\n\\n        self.test = datasets.CIFAR10(\\n            root=self.data_path,\\n            train=False,\\n            transform=self.test_transform,\\n            download=False,\\n        )\\n\\n        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\n\\n    def train_dataloader(self):\\n        train_loader = DataLoader(\\n            dataset=self.train,\\n            batch_size=BATCH_SIZE,\\n            drop_last=True,\\n            shuffle=True,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return train_loader\\n\\n    def val_dataloader(self):\\n        valid_loader = DataLoader(\\n            dataset=self.valid,\\n            batch_size=BATCH_SIZE,\\n            drop_last=False,\\n            shuffle=False,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return valid_loader\\n\\n    def test_dataloader(self):\\n        test_loader = DataLoader(\\n            dataset=self.test,\\n            batch_size=BATCH_SIZE,\\n            drop_last=False,\\n            shuffle=False,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return test_loader\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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       "                        break;\n",
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   "source": [
    "import os\n",
    "\n",
    "from torch.utils.data.dataset import random_split\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import transforms\n",
    "\n",
    "\n",
    "class DataModule(pl.LightningDataModule):\n",
    "    def __init__(self, data_path=\"./\"):\n",
    "        super().__init__()\n",
    "        self.data_path = data_path\n",
    "\n",
    "    def prepare_data(self):\n",
    "        datasets.CIFAR10(root=self.data_path, download=True)\n",
    "\n",
    "        self.train_transform = transforms.Compose(\n",
    "            [\n",
    "                transforms.Resize((70, 70)),\n",
    "                transforms.RandomCrop((64, 64)),\n",
    "                transforms.ToTensor(),\n",
    "            ]\n",
    "        )\n",
    "\n",
    "        self.test_transform = transforms.Compose(\n",
    "            [\n",
    "                transforms.Resize((70, 70)),\n",
    "                transforms.CenterCrop((64, 64)),\n",
    "                transforms.ToTensor(),\n",
    "            ]\n",
    "        )\n",
    "        return\n",
    "\n",
    "    def setup(self, stage=None):\n",
    "        train = datasets.CIFAR10(\n",
    "            root=self.data_path,\n",
    "            train=True,\n",
    "            transform=self.train_transform,\n",
    "            download=False,\n",
    "        )\n",
    "\n",
    "        self.test = datasets.CIFAR10(\n",
    "            root=self.data_path,\n",
    "            train=False,\n",
    "            transform=self.test_transform,\n",
    "            download=False,\n",
    "        )\n",
    "\n",
    "        self.train, self.valid = random_split(train, lengths=[45000, 5000])\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        train_loader = DataLoader(\n",
    "            dataset=self.train,\n",
    "            batch_size=BATCH_SIZE,\n",
    "            drop_last=True,\n",
    "            shuffle=True,\n",
    "            num_workers=NUM_WORKERS,\n",
    "        )\n",
    "        return train_loader\n",
    "\n",
    "    def val_dataloader(self):\n",
    "        valid_loader = DataLoader(\n",
    "            dataset=self.valid,\n",
    "            batch_size=BATCH_SIZE,\n",
    "            drop_last=False,\n",
    "            shuffle=False,\n",
    "            num_workers=NUM_WORKERS,\n",
    "        )\n",
    "        return valid_loader\n",
    "\n",
    "    def test_dataloader(self):\n",
    "        test_loader = DataLoader(\n",
    "            dataset=self.test,\n",
    "            batch_size=BATCH_SIZE,\n",
    "            drop_last=False,\n",
    "            shuffle=False,\n",
    "            num_workers=NUM_WORKERS,\n",
    "        )\n",
    "        return test_loader"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1c0895b",
   "metadata": {},
   "source": [
    "- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the dataset loading -- if we run our code in a distributed setting, this will be called on each node / GPU. \n",
    "- Next, let's initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "543677b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 12;\n",
       "                var nbb_unformatted_code = \"torch.manual_seed(1) \\ndata_module = DataModule(data_path='./data')\";\n",
       "                var nbb_formatted_code = \"torch.manual_seed(1)\\ndata_module = DataModule(data_path=\\\"./data\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
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       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
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    "torch.manual_seed(1) \n",
    "data_module = DataModule(data_path='./data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7ed6931",
   "metadata": {},
   "source": [
    "## Training the model using the PyTorch Lightning Trainer class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61a73a12",
   "metadata": {},
   "source": [
    "- Next, we initialize our model.\n",
    "- Also, we define a call back to obtain the model with the best validation set performance after training.\n",
    "- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. However, here, we will keep things simple and use the `CSVLogger`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "256d8185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 13;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\nfrom pytorch_lightning.loggers import CSVLogger\\n\\n\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\n\\ncallbacks = [\\n    ModelCheckpoint(\\n        save_top_k=1, mode=\\\"max\\\", monitor=\\\"valid_acc\\\"\\n    )  # save top 1 model\\n]\\nlogger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\nfrom pytorch_lightning.loggers import CSVLogger\\n\\n\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\n\\ncallbacks = [\\n    ModelCheckpoint(save_top_k=1, mode=\\\"max\\\", monitor=\\\"valid_acc\\\")  # save top 1 model\\n]\\nlogger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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   "source": [
    "# %load ../code_lightningmodule/logger_csv_acc_basic.py\n",
    "from pytorch_lightning.callbacks import ModelCheckpoint\n",
    "from pytorch_lightning.loggers import CSVLogger\n",
    "\n",
    "\n",
    "lightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\n",
    "\n",
    "callbacks = [\n",
    "    ModelCheckpoint(\n",
    "        save_top_k=1, mode=\"max\", monitor=\"valid_acc\"\n",
    "    )  # save top 1 model\n",
    "]\n",
    "logger = CSVLogger(save_dir=\"logs/\", name=\"my-model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53dc9e43",
   "metadata": {},
   "source": [
    "- Now it's time to train our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cc14f439",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\n",
      "  rank_zero_deprecation(\n",
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name      | Type        | Params\n",
      "------------------------------------------\n",
      "0 | model     | MobileNetV3 | 1.5 M \n",
      "1 | train_acc | Accuracy    | 0     \n",
      "2 | valid_acc | Accuracy    | 0     \n",
      "3 | test_acc  | Accuracy    | 0     \n",
      "------------------------------------------\n",
      "1.5 M     Trainable params\n",
      "0         Non-trainable params\n",
      "1.5 M     Total params\n",
      "6.112     Total estimated model params size (MB)\n"
     ]
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       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 14;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/trainer_nb_basic.py\\nimport time\\n\\n\\ntrainer = pl.Trainer(\\n    max_epochs=NUM_EPOCHS,\\n    callbacks=callbacks,\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\n    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n    logger=logger,\\n    deterministic=False,\\n    log_every_n_steps=10,\\n)\\n\\nstart_time = time.time()\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\n\\nruntime = (time.time() - start_time) / 60\\nprint(f\\\"Training took {runtime:.2f} min in total.\\\")\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/trainer_nb_basic.py\\nimport time\\n\\n\\ntrainer = pl.Trainer(\\n    max_epochs=NUM_EPOCHS,\\n    callbacks=callbacks,\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\n    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n    logger=logger,\\n    deterministic=False,\\n    log_every_n_steps=10,\\n)\\n\\nstart_time = time.time()\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\n\\nruntime = (time.time() - start_time) / 60\\nprint(f\\\"Training took {runtime:.2f} min in total.\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/trainer_nb_basic.py\n",
    "import time\n",
    "\n",
    "\n",
    "trainer = pl.Trainer(\n",
    "    max_epochs=NUM_EPOCHS,\n",
    "    callbacks=callbacks,\n",
    "    progress_bar_refresh_rate=50,  # recommended for notebooks\n",
    "    accelerator=\"auto\",  # Uses GPUs or TPUs if available\n",
    "    devices=\"auto\",  # Uses all available GPUs/TPUs if applicable\n",
    "    logger=logger,\n",
    "    deterministic=False,\n",
    "    log_every_n_steps=10,\n",
    ")\n",
    "\n",
    "start_time = time.time()\n",
    "trainer.fit(model=lightning_model, datamodule=data_module)\n",
    "\n",
    "runtime = (time.time() - start_time) / 60\n",
    "print(f\"Training took {runtime:.2f} min in total.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40326150",
   "metadata": {},
   "source": [
    "## Evaluating the model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "819f9169",
   "metadata": {},
   "source": [
    "- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (PS: you may want to check out [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) later on, which take care of it for us):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "88d7cc4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AalEE+kCeNBYLIUQ9XpMIAIJsitziOlf/0lgshHCHohz49FYoOOLuSAAvSwTBNtVA91GpGhJCtKLNX8DWr2DXYndHArgwESil3lJKHVVKbW7g9TFKqTylVKrj9mdXxVLNlAicNRZLiUAI0YLsVXB0W8Ov7/zW3De2TytyZYngHWD8KfZZprUe4Lg97cJYAAiyndBGUFM1VOH8ACGEd0tfDju+rX1+YDX8+sGpj/vlPzDzAji25+TXygrM+wIc3doycZ4hlyUCrfVS4Jir3r85gnyVNBYLcTarauW1Q757Ehb8vvb5sufhm0cbP2fYq2DNm6DtsHPRya/v/tH0VIxI8pgSgY+bP/98pdQGIBOYobXe4mwnpdR0YDpAbGwsKSkpzfowX13B8SLFj8nJWJTCUlXOKGBP2nb2lzfvPVtaYWFhs79fa5EYW8a5FqOtPBetfKi0Bbd4HP4lR0ja+wFtsn4hrevvONTu0mbFeDp8KgoZcXgTCs3yxfOptIUyfN8a/KvKWL/gHfLDutfsG5KfRtvDS9jd+XbCczfRL28/dmUld9UcCjvPqBdfj21vE+UTzIHwkXTa+z7LF39NpS0EAGtlMQNS/8DuzreTG9G/xb9Tg9+11T7pZOuBjlrrQqXUBGAe0NXZjlrr14HXAQYPHqzHjBnTrA/8Pn0xmnIGnz+SUH8b2O2wDDp1iKdTM9+zpaWkpNDc79daJMaWcU7FaK+CVwZDeEe4dV7LBrH7R/joflAWiO1F952v0r1dGIz+H1DKdb/jjm/hZzNb8cjOYdC2L6RkA3Bemwq4oM5nfvAfyPyB+IgAKC+C4FgsvSYRue5dQgNsjKqOz14Fq++AnhPo1O9q2Ps+I7tFQccLzOsbP4PCvQwoWQHXPNTy36kBbus1pLXO11oXOh4vBGxKqWhXfmaQGVNWO9+QxQIWH2ksFqIxTVnTe/sCUx++JwXyDrbcZ5fkwrz7ILITPLAefvsj9P8NpPwdlv371McXHzMXfM2RvtycHwAObzK3agdW1j4uOGySVVRX2PIlpH0PA2+B7pdDVRnhuXWPWwXFOdB9PMT0NNuO1KkI2f6Nud+12MTeStyWCJRSbZVSyvF4qCOWHFd+ZpBNAU5GF0tjsRBOKXsFvNgPVs9ufMcVr0JQG0DD5s+d77PnJ/j55Ybr+QsOw8ZP62/7/g9QeASunglh8WC1wdWvQd8p8OPfYFMDnwVQmmdiX/lq47E3JH0ZdDgfQuLqJ4LOF5lG4+oEuekz0x5w48cmSfkEwKDboOMIsAUSlbO29j03fAy2IOh6GYS2A7+w2naCilLY9QMkDDXrqG+Z27y4m8GV3Uc/BlYA3ZVSGUqpO5VSdyul7nbsMhnY7GgjeBmYqnVTLj2aL7g6EZw48Zw0Fgtnygph63x3R+F6ZYW1j/MzTWPosb0ABBemQ95+SP5/UJrv/PiMdeYKeeSjkDDk5JM5mJPmgkdh8Z/gg2ugKPvkfVbOhC9/C4VHzfP0nyH1Qxj5CMSfV7ufUjDpFXOinXcPwQVOeuaASTzlBbDu3aaVauoqyTUn/o4jTJXQ4U1wZLNJCj2uMMkpd5/Zd8MciB8M0V1NknpkC4R3AB8/6DSGyGPrzOeXF8PmudD7avALNt8jpmdtIti7FMoLTZVXmx7Of0cXcWWvoRu11nFaa5vWOkFr/abWepbWepbj9Ve01r211v211sO11r+4KpZqDZcIJBEIJ9a+CZ/eAkc8o4ufS6x4DZ5tD+9dDakfwetjzPde+xYAofk7zH4lx2HVrAbe4z/gFwoDb4Z+N5gT5uEThg/t+wVydkHva2H/KnhtOPzyiqlPr3Zog7nP2m7u9y4FFFz46Mmf6eMHN3wAvsF02vOu87h2/2juc9Ig89eTXy/NNyWKzNSTX9u/0lzlJ440iSBrO2SsMY/bDzP7HFgNhzaa7zvgRrNNKQiKqn2frpcSUHoUdiw01T7lBTDgN7Wvx/Q0XUi1Nq/7hkDSKOh7vUmux9Odf7cW5lUji2sSwYldSGVksXCmuq93xupT71vUxFrNtW+ffJI8HXa7aXA80bav4d2rml7NabfDt0/A90+a6o/DG2HePWALNFejju8emr8DQtpBj4nmxF1yvP777P7RVGEM/S34h0Lva0y9evLfzWvVpY3175lkMekVuGsxxPSCRX+E2ePMSVDrOonAkXyOboXIJPANcv4dAiNh1Awij6fC7uT6r2kNu5dAx5HmYm/jJ7WvVVXAghnwXDf44k6YfRGkPFu/yip9mRlwmjAY2vYDXWUSWdu+JnbfYPO9v7rXvH/va53H2P9GCoI7w9x74JeXTWN6hwtqX4/pBaW5sCfZJIuul5gk1/d6QJm4qksze5c1/d/ZafKyRGDu8+rNN+QrJQJPUpoHs0bCvhXujcNeZa4KATLWNr5vzm54rgusf7/x/favgm8ehm//p/lxzb8fZo44uQH055dh70+wa0nT3if5/8GqmTD8Xrjta3hoI1w7G6YnQ8+r4FAqlOYRmr8TEgbBmCehLA9WvV77HqX58NUDppF01GNmW1A0DL4TdiyA96+BV4eaE9jWedB3sjmpx/WH2+bDZX+HrG2maiQvA0ocjaPZO8191nZzomzMkLso9YuBH/4Cx/eZv4XWpuE6d7+phuk+3lz5V1WYv+vc38Ga2SaeaQvMfcr/wRvjzN96zRuw6r/mytwWYE7+1dr2BYvVJIgdC81n3PCBSUrO2PzZ0vtx0zHl8CZTGrDUOe1WNxi/76guG3CTeR7R0VQRbfjYlM5WvArvXWXaRVzA3eMIWpWPRRHka5XGYk+2J8X8h0n7Hjqe3zLvqbUp5lusTT/m8EYoyzf/Pk6VCKqrEX74C/S6CvzDnO+X/Iy53/dz7dVvQ7J2mHrojiNq4077wdSZg2lU7OboS39sT22pZcNH5sTXmK1fwbLnTM+Wy/5uqjP8gqHfFPN60oWw9J+w7WsCSg+bev+2fUwj6a8fmJO+xWLq+wsy4Y5F5oRZbcI/YeyTJvEteBTenWi2n3dr/Th6TDSNwenLIDTebLMFmgRQUWpO6r0mNf5dfPzYm/Qbem5/EV7qZ7adfz9EJJrHXcaZRtmtX5kST+FRkzAveRpGOLpnJo6EbuPN4LE3xjmOuwSudSS9iCRTAigvNKWD6u9i9YUJ/6r9rAaUBsTC5LdgydOm+qyuDufDxX81MXYYbtoWqo1+HA6uM79h9e91qSSCFhEe6OukakhKBB5jz0/m/kyqT+qy2+GTm8yV27RvICCiacel/2zuB95kqnNK80xS2DYf+kyuf1WX+as5KRRnmy6NlziZLWXPT6bOe8wfTBXBylkQcYPzzy7Nh3evNIkgKMZUEwy40ZwQorqaKQrWzK5NBBs/M/c9rzR934uPNXyFemyPqaZIGAJX/NskgRMlDDXf9eeXHM+HmPsBN5mqlPSlpofQundNiaL9kJPfIyDCJKR2A+Gz20x1UbuB9feJ6GiqSvYuNVf+ygLdLjOlwZw0Ux3Tpofz71HHkdjR9OzZ0/S02fcLrHgFgmPNCTqyE4QmQHR32PEd+IXApc/ABQ/Uf5M+15pqmeUvmkR+/v21f2OLBWL7mAuUiCTH/teZW1N1vsjcTmT1gZEPOz/GYjWltE9vhU6jYeTv6/+7a0FelwjCAmzSWOzJ9joSwZEWSgSrXzdFeBR8cgvc/IWpg3Ume5epl+55pblqj+xkrsLWvgUH15ursx//Zq5+e15Ze9yhVHOyDO9oer4Mut3UbYMpjWSsMfXhIe3MVWhxDqx7G9+hlzoNg5RnzZXrZX83pY3Vr9d2gZy2wFS1/PQP07MnItHUfydeaK7Ut31tZrYsOGR6nVz3hrnSrLb8BXOCvf7dhn8Hmz+0Hwrpy9BYUHEDzPYeE81J8tcPTCOvXyiMmtH47x8SC3d813Bf/qQLYds35mIsujvEDTB179XVcqeqGgKTQPpPNY/7TTUJdNcPpooKzMXe/U1o5/ELgXF/cv7a0N+a39tFJ+IGBUaaCxgX86o2AoDwQBt5J3UflcZitykrNA13eQdNPXHOLgjrYE5kZ9Iwpu2mW+PiP5ti/7WzTRXEwsdO3rfkOHz9kKnP/vQW+PlFc2XZcQTEDzL7pC+v7TVTt+96VYW5Umw30JxE7FWw3tGLpaIUXh8Nb14C2Wlw2f8zJ9lhv4OqCrqm/deUNI7tgY9/Y27r3zOfM2ganH8f3PC+6Y540f/C+H+YaoxBt5mT388vmgFMx3abap22/cyJ89vHTcmkrMD0Bkr7wcRTcNh0dRxwk+mT35jEkQAUBieCb6DZZvM3/fe3zDXJdcQDDZc8TtTQCTRpdG1jaVz/2hLA1q9MKSKqS9Pev5rVx1TD9LvB/E4tpe9kGO3k3845wisTgZQIPMiOhaaa47vHa6uFhjuGmhzZ1PBxDbHbYd59jFo6Bd64yPRkueoV6He9Oblu/OTkqsDk/zMNvUPuNHXSP/zFnJwSR0JAuLlSXfEqFGWZK9ad35uTLJj67MpSkwhC20HnseaKXGtzdX5og6kqmpFmqh8AojrDRf9LdPYqeHWYafxNX2bq+ec/YK66x9WZlT0k1lztV/8uoe1MiWTdO/D5HWYAU8+rTDXPkLtMlcKVL8H9ayG6C3x8Q22CsVeaBHMqiRcCkB/avf72gTeZ9whqA8PuOa0/TWOfg73SkQi6mef7fjZJoHpiyNPhH2bq9+Nab66es50XVg2d0EZg9ZXGYnfa+Z253/Y1ZO00J5i+U0wj4uHN0GmM8+PsVaZ7Ypvu9RvYfn4BUj/gaOxFtB080TQWBrcxr3W52Jw8D20wVR/g6L+9wEwHMOFfJkkUHzMlgI4jzD4JQyB7hxk0dOkz8PZ4Uxffb0ptH/Tq+u8+15lGyYPr4Nf3THXR+Q+cfEU8agbrc0MYlPGuOWFd8TwERpk2iPCOp77SnviCaTuw+Ji69oBws33wHaZnSnXj7bQF8Nk0k2AsNpMwojo3/t7V37nb5RwNHEW9skPcAJNQk0abBuYzFRpn2j1y0szvEN4RfPxNcq3uUSNcrkklAqVUkFLK4njcTSl1lVLK5trQXCM80EZecQU1g5ilsdh9qipMXW6fyeYEkL3DnGCC20Bw24bbCTZ8Aq8MgQ8nw8c31vb/3pMCPz4DfSazvceD5gq/bo+O9o668n11xi4e3gT5GSYRgKk3/80ncMf3EN7ecZyjMXTkw2YwUWhCbfVQ5q+mrry6EbHHFebiYulzphH0vFsarBYpCO0O9/5iPi8s3lS99JsCHYad+rcLjISeE+vPWQOmVFC3B49/GPzmMxjyW1OdNPKRU783mP8Xv5lDXvgJdfRKmdJGdemmJSSNAlRt18wox9yTbSQRtJamVg0tBfyVUvHAEuB2zMIzZ53wABvlVXZKKhyDcqRqyH0OrDJ15L0mmYZRMFfwYLornthzyG43XfzmTjdXoyMeMsli9etm9O+nt0F0N3OictYbJriNqW6obogEx6Ijysz9Us03qP7JuN9UmPqRaSy1WKDPNWawUl6GSQRx/WtP9v5h0PVSswKVstT2C3cnqw9c8Rw8sQ/aDXB3NCcb/Tjc9JmpxgNTygMpEbSipiYCpbUuBq4F/qO1vgZoQnO+5wkPNAWZmnYCaSx2n53fmeqKzmPN1e1vk00jH5juelnb6/9t5k6Hla+ZuunfJpv+110uMYOj3r/GXAn/5pPGqyw6DDdD96t7sexYaKqJqquPnLH5myv96uQy8FYT9+tjTCI6sVtk9dVyl0tMfb6nqFtS8CQhsabrZjVJBK2uyYlAKXU+cBOwwLHtrGxfCAswjU/Hq0cXS4ngzFRPztVUhVnw/R9Nw/DO7yFxhOm2B2ZiserBU237gr2idpTp0e1mlscRD8Plz5r9lDKDl6oqzN/wlrmnHNxDhwtML6Hsnaan0qHU2mqhpmrTDaanQGC0WWmq7oRoAN0uN33Gnc2RI05t4M2mhHi6PYZEszX1ZP4w8CQwV2u9RSnVCUhu/BDPFBfmD8DB4yX0bhcmjcVn6tv/gS3zYMbO2gbLukpyYf8KU/ViscCCR0zD8IpXzOuD73D+vrF9zP2hVFNNtHUeoGD4CT1VIjuZBtHAyKY1glb3qd//S+1smt0nnPq4E8X0MHPjp31vGmDr8g00SUk0T2i7pvVsEi2mSYlAa/0T8BOAo9E4W2v9oCsDc5XEKDOBVXqOY9ZDaSxumqoKc6vuUw6mn/+WueaqeOd3ZlDPsb2mi2ZXx2Cpz6aZ6Xr732h67Wz72oyuDW9vJgrre73zz4vuahqQ175t6tm3zHPMDd/25H2djWxtSGQnM1r3p3+Z6RESLzTtCs3hG2gmWRPiLNfUXkMfKaVClVJBwFZgh1LqrBxdERZoIyLQRnpOsdlg9TNVEM1dxcgbVJabKQ9e7GN65lTb8JFJAn5h5kQNsOh/zQRes8eam7bD0Olm8qwv7jLdDy/8venieN1sM0mZMxYrjHgQDjomAcvaZiYQO1NKmWUBCzLhvNvgps+dNywL4UWaWjXUS2udr5S6CVgIPA6sA/7lsshcqGNUEOnZdUoEYE5oFn/3BeXJvnvCVO+EtYf3r6Fjxxuh8gJztd5+uKkjX/OGGcm7fQEMv89UnRzfZ4r4gZHmqnv5i2YaYmsT/9kNuBlS/mE+H3VyFUxzXfqMGXXqbO4XIbxQUxuLbY5xA1cDX2mtKwCXribmSknRQeyrWyIA72kwrig9vf1TPzYLlVzwINy7EnpdTVL6h/DyQDO1weA7oNfVJpF+eqvph3/ho2Z2xnF/qh0YNfS38OiW+lP6norN3yQSe6Wp2w+NO73YGxLeXpKAEHU0NRH8F0gHgoClSqmOQAPr1nm+xKggMvNKKK2oMo3F4B0NxhWl8J9B8OX0pi3dp7WZjjh+MIx7ynTLvP5tNvX5g6lOCY41YwAShpgJ1fIzTI+Phqp7mmPwHWaA0aDbW+49hRD1NCkRaK1f1lrHa60naGMfMNbFsblMYnQgWsP+Y8W1VUPe0GCctsicrDd+Asufd75P8v+ZeXUAjmwxE6INvLledU5O9DAzj829K81Vu8ViGk2V1Uzf25L8Q+GBtdC/gSmbhRBnrEmVtUqpMOApYJRj00/A00Cei+JyqZqeQ9lFdPOmqqHNn5u5fBIvhCV/g9i+tXPag+njv+w5M1iq/41m3htlMSNqT2TzN7dqY580vYaqp18WQpw1mlo19BZQAExx3PKBt10VlKvV60JaUyI4x0cXl+abAVy9r4FJr0Jsb/j6wdq+9GAGbNkrobIE1rwJW+ebAViNjbqt5hcCcf1cF78QwmWamgg6a62f0lrvcdz+CnRyZWCuVK8LqbeUCHYsNDM69pls+r9f+bKZnz7577X7pH5kpkvocolZRStrm1l6UQhxTmtqIihRSo2sfqKUGgGUuCak1lHThdQbGou1NqtVhXeonX45YZCZnXP1f+HAajM185FNZvDWBfeb9Xqh/kpcQohzUlPHEdwNvOdoKwA4DrTg8j+tLyk6iFV7cmqrhoqyzBqs/aZ47uRcpyt7F2z/xix4nr0TRv1P/cFTF/3J9Pt/6zIISzBJse9k8A83JQNboGdNmiaEcImmTjGxAeivlAp1PM9XSj0MbHRhbC6VGBXE3F8PUkY4fmD6wFeVm8bR825xd3hn5vg+M0//0S3mecJQ0y7Q74SeNwHhZvK0la+ZwWF9r69d3P3Wr1ozYiGEG53WDKJa67pjBx4FXmzRaFpRYrSZMyezIpgkMGu9Ht1qFkc521RVmtWw4gebvv3vTYKSYzDhOTPnT0THho8NaWuWUhz7vyYJVvMPa/gYIcQ55Uymkj6rJ2jp3MbMWb+lrA1JD6w30xfPGmmqU842m7+AbxwrT/mGgK6CW+ef3mRszVkbVghxTjiTxevP2ikmALrEBONjUWw7lG+mL7ZYzfzn1fPfu1PeQVj2bygvOvW+WsOqmWb07fhnzWpZN358eklACOHVGk0ESqkCpVS+k1sBcFa3IvrbrHSJCWZLZp3aruhucDzd/WMKfvkPLHka3rzUxFNX6Qlj+DLWmOUSh/3OzNV/+4KGF3wXQggnGk0EWusQrXWok1uI1vqsXKGsrl7tQtlaLxF0NdUqx/e6LygwC7pHd4O8AzD7Isg/ZLZv/BSe7QirZ9fuu3KmmQa6/43uiVUIcdY7k6qhs16vuFCOFpSRVeAYTBbd1dxnp7ktJv+SI5CTZiZbu2MRlBXC4j+ZaqLFfzZVWAtnwC+vwLLnYetXppdTY+v0CiFEI876q/oz0bud6Rmz9VA+o0PamHp2cGs7QeSx9eZBl4tNYhrxkJkBtKwACg6ZRuCfX4JFfzT7dTgfLnjAbfEKIc5+LksESqm3gInAUa11HyevK+AlYAJQDEzTWq93VTzO9IoLBWBLZh6ju7UxM10Gt4Uc9/Ucijz2K4R1qF24e+QjsHGOWQqy19XQabQZHbxlnpmjXyZ5E0KcIVdWDb0DjG/k9cuBro7bdGCmC2NxKizQRkJEwMntBK4uEfz8kunrX1VZf3tlOeG5G6HLuNoRwL6BMPFFM87h4r+YbbYAGHCjJAEhRItwWSLQWi8FjjWyyyTgPcf6BiuBcKVUCy1B1XS94pw0GGenNW3hloZ8+7jp9eNMZblJBHtSYP279V/LWI1PVYmpFqqryzi4d4Wc+IUQLuHONoJ44ECd5xmObYdO3FEpNR1TaiA2NpaUlJRmfWBhYeFJxwaWl7M3u4LvfkjG30cRn2uha2kuPy/+igrf8NP/EG1n5Nr3qfQJYKV11Ekvtzn6M72LcyjzjcTy/VOsyo2l0haMT0U+/Tc8RaDFl18OWqg60rzv2Bqc/Y6eRmJsGRLjmfP0+ADQWrvsBiQCmxt4bQEwss7zJcCgU73noEGDdHMlJyeftG3RlsO64+Pf6DV7c8yGtMVaPxWqdfrPzfuQrDRz/FOhWucfMttWva711w9rXVGm9XtXa/3vXlof/FXrp8K0/vQ2rVPnaP3q+Vo/3UZv+PzfzfvcVuTsd/Q0EmPLkBjPnKfEB6zVDZxX3dl9NANoX+d5ApDZ2kEM7BAOwIrdOWZDTC9zv2FO897w4Lo6j9ebKqafX4K1b8HHN8DuZLP0Y7sBMOxu2DIX5k43S0L+5hOORZ3X7O8ihBDN4c5EMB+4VRnDgTyt9UnVQq4WHexH/4QwknccNRtC25kum+vfhc1fOj/IXtXwG2auB58As37vwXXmBJ93wHTz3P2j2WfgzeZ+/P/BI1vg/nXw6FbofNYuAy2EOIu5svvox8AYIFoplYFZ89gGoLWeBSzEdB3dhek+erurYjmVMd1jePnHNI4VlRMZ5Gvm6d+3AuY/aFbtyt4BY/9o1uQtPgavjzFTNo/708lvdnC9udovLzRJIaSt2T7pVdi7FIpzINxREFLKrAMghBBu5LJEoLVudM4DR53Vfa76/NMxtkcMLy1JY1laFpMGxIPVBpPfgveuMoO4LDZYMAMSR8JP/4Tcfaa6Z+BNENkJMlPNovDBMXB4Iwy5ywwA2zrPlA7CO5j9ojq7+6sKIcRJvHqKiWr94sOICvIlZUdW7cbw9vDgr3DPz3DzF2YOoo+mmiqj/jeaZLHkb2Zg1+yL4P1rTEKoLDWre8UPMhPEpX1vJoFTZ/Ws3UKIc5hXTzFRzWJRjOrWhp92ZlFl11gtJ5y0I5Ng3J/huycgrL1Z8CWsvZn6YetX5mo/ewfMv9/sHz+odgppeyV0krp/IYTnkhKBw5jubThWVM6GjFznOwydDhf+Hia/bSZ4G/EghMab6R6mJ0Of6yBrOwREmkVu2vQw1UIoSBrdit9ECCFOj5QIHMZ0i8HHoli05QjndYg4eQeL1ZQKqvmFwH2rzQLvFotZFGbXD5AwxFQDWX1MkigvgqCo1vsiQghxmiQROIQF2rigSzQLNx3i8fHdUU2p06879XNwDNy1xCSGate9Cdre8sEKIUQLkqqhOib0acv+Y8X1Vy07HdFdISy+9nlwGwiJbZnghBDCRSQR1HFp77ZYLYpvN7f6uDYhhHAbSQR1RAb5cn6nKBZuOlw9/5EQQpzzJBGc4PK+bdmbXcT2wwXuDkUIIVqFJIITXNa7LUrBoi1H3B2KEEK0CkkEJzCT0IXXTkInhBDnOEkEToztHsOGjFxyCsvcHYoQQricJAInLuoRg9bw086sU+8shBBnOUkETvRuF0qbED9+3C7VQ0KIc58kAicsFsWYbm1YujOLyioZGSyEOLdJImjART1iyC+tZP3+XHeHIoQQLiWJoAEju0bj62Phm42tvoyyEEK0KkkEDQjxtzG+d1u+Ss2ktKKRNYqFEOIsJ4mgEVMGtyevpILFW2VwmRDi3CWJoBEXdI4iPjyAT9cecHcoQgjhMpIIGmGxKCYPSmD5rmwyjhe7OxwhhHAJSQSnMHlQAgq44501rNid4+5whBCixUkiOIX2kYHMvnUwxeVV3Dh7Ja8m73J3SEII0aIkETTBuJ6x/PDoaCb2i+O5RTtYliZTTwghzh2SCJrI32bln5P70TUmmIfmpJKZW+LukIQQokVIIjgNgb4+zLx5EKUVVfxl/hZ3hyOEEC1CEsFp6twmmOmjOrFo6xE2HMh1dzhCCHHGJBE0w50jk4gItPHcoh3uDkUIIc6YJIJmCPG3cc+YzixLy2blHulSKoQ4u0kiaKZbz08kOtiP2Uv3uDsUIYQ4I5IImsnfZuW68+L5aWcW2bKkpRDiLCaJ4Axce14ClXbN1xtkqmohxNnLpYlAKTVeKbVDKbVLKfWEk9fHKKXylFKpjtufXRlPS+veNoTe7UL5cv1Bd4cihBDN5rJEoJSyAq8ClwO9gBuVUr2c7LpMaz3AcXvaVfG4yrXnJbDpYB5pRwrcHYoQQjSLK0sEQ4FdWus9WutyYA4wyYWf5xZX9W+H1aJ4/IuNfLhqHznSXiCEOMsorbVr3lipycB4rfVdjue3AMO01vfX2WcM8AWQAWQCM7TWJw3ZVUpNB6YDxMbGDpozZ06zYiosLCQ4OLhZxzbm+/QKFqVXkFOqCfVV3DvAjx6R1ma9l6tibEkSY8uQGFuGp8foKfGNHTt2ndZ6sNMXtdYuuQHXA2/UeX4L8J8T9gkFgh2PJwBpp3rfQYMG6eZKTk5u9rGnYrfb9cYDuXrsc8m605ML9JzV+5r1Pq6MsaVIjC1DYmwZnh6jp8QHrNUNnFddWTWUAbSv8zwBc9VfNwnla60LHY8XAjalVLQLY3IZpRR9E8L46r4RXNA5ij9/tYUDx2QxGyGE53NlIlgDdFVKJSmlfIGpwPy6Oyil2iqllOPxUEc8Z/VQ3RB/G/+c3A+LUjyzYKu7wxFCiFNyWSLQWlcC9wPfA9uAT7XWW5RSdyul7nbsNhnYrJTaALwMTHUUYc5qcWEBPDCuC99vOcJPO2XtAiGEZ3PpOAKt9UKtdTetdWet9f9zbJultZ7lePyK1rq31rq/1nq41voXV8bTmu4cmURSdBB/+HKTjDwWQng0GVnsIn4+Vl6eOpDswjLu/WA95ZV2d4ckhBBOSSJwob4JYfxzcj9Wpx/jznfX8OmaA1I6EEJ4HB93B3CumzQgniP5pby+dA/L0rKJCfHju4dHERnk6+7QhBACkBJBq5g+qjNr/ngxc6YP53hxOX+at5lzoE1cCHGOkETQSpRSDO8UxcMXd2PBpkPMlxlLhRAeQhJBK/vdqE4M7BDOjM828M/vtlNSXuXukIQQXk4SQSvzsVp487YhXNU/ntdSdjP2uRTeXL6X4vJKd4cmhPBSkgjcIDLIl39P6c+nvzufjlGB/O2brYz8RzJv/7yXCru0HQghWpf0GnKjoUmRfPK781m37xj/XrSTv369lbggxbALKgj1t7k7PCGEl5ASgQcY1DGSD+8axuu3DOJwkeaZb2SOIiFE65FE4CGUUlzauy0Tkmx8ujaDlB1H3R2SEMJLSCLwMJO62OgaE8yMzzby7i/p5JVUuDskIcQ5ThKBh/G1Kv7zm4G0C/fnqflbOP//lvCP77ZzvKjc3aEJIc5R0ljsgXq0DWX+/SPZlJHH7GV7mPXTbt5fsY87RiRy54Wd8LdZKK2wExYgDcpCiDMnicCD9U0I4+UbB3Lf2C68+MNOXv5xF6+m7KbKrlEK3rl9KKO7tXF3mEKIs5wkgrNA97YhzLx5EFsy8/h6wyGCfK3M/fUgf5y7iUWPjCLQV/6MQojmkzPIWaR3uzB6twsDzBiEG15fyYs/pPGHCT3dHJkQ4mwmieAsNaxTFFOHtOfN5Xs5nFfK8E5RTOwfJwPRhBCnTRLBWezJCT2pqNIsS8ti/oZMnv12G3dd2IlpIxIlIQghmkwSwVksLMDGv6f0R2vNxow8/vPjLp5fvJM3lu3hzpGduHpgOzpEBqKUcneoQggPJongHKCUon/7cN64bTCbD+bx0pI0XvhhJy/8sJO4MH/iwwMIDbBxcc9YJg9KwNen8eEjWmue+GITlXbNP67ri49VhpsIcS6TRHCO6RMfxuxbB7M3u4jlaVmsTj9OdkEZ6TlF/GHuJl5N3kWHyECyCssYlhTJ7y/tzrGicl5YvJMOUYH8/pJuvL9yH5+sPQBAgK+Fv03qI6UKIc5hkgjOUUnRQSRFB3HL+YmAucpfmpbN7KV7KK2oIiEigDlrDjB/QyYl5VX4WBWlFXbWph9jw4E8Lu4ZQ+eYYP770x58LBZ+O6oTYQE2lmw7ws7DlYzWWpKDEOcISQReQinF6G5t6g1A23G4gJeW7CQ62I8Hx3Xlx21H+eO8TUQH+/Gvyf0JC7CRV1zBO7+k8+6KdGwWC+VVdgB2lq/jn9f1IyLI111fSQjRQiQReLHubUN47aZBNc+nDGnPeR0j8LdZak7wz17Xj/vGduHL9QcpLKvg0t5t+TJlHZ/vOMplLy7lj1f05Kr+7cgpKudwXimxof5EBflisUhpQYizhSQCUU+XmOCTtrWPDOShi7vWPC9KtHHTJUN58stNPDQnlb9+vZVjdSbFUwpsFgsRQTZuGNKBKYMTKK+0U1xeRZeYYPxt1pM+40h+KX+Zv4Ur+sVxRd84qXYSohVJIhDN0ic+jHn3jWDOmv2s2XuMXu1CaR8RyNGCMrILy6io0qQdKeDlJWm8vCSt5jhfq4VubYMpr7RTWaV56OKuXNIrljvfXcPmg/l8u/kwn3fPIC4sgIzjxQAE+/lw9cB4LuvdlsKySj5YuY/oYD+u7B/HrqOFvLA4DZtVcf3gBIYkRjpNNEKIhkkiEM1mtShuGtaRm4Z1bHCf3VmF/LQji/BAG34+VjZk5LLtUD5Bvj5k5Bbz0JxUEiICyMwtYfatg9mXU8SLP6Th65NH+4gAlFLsOlrIt5sPM6pbG7YfyudoQRkAf1+4jePF5YQH2LAoxbebD9d8rkVBwI/f0Ts+jCcv78HADhEUllWydGcWc389SG5xOVcNiGdMtzZUVNnxt1lpFx5wyu9st2vmrDnAx6v38/Sk3gzsEHHmP6QQbiaJQLhU5zbBdG5TW910Rb+4mseVVXZe+GEns37aw1NX9uaSXrEA3DkyqV7VUEWVnTeX7+XFH3bSLTaE/94yiILSSj5evZ8OUYHcO6YLATYrKTuOkp5TRFmFnR2799ImLp5vNh7imtd+IS7Mn0N5pQDEhPgREejLn+Ztrhdr15hgRnVrQ5eYYBIiAgj1t2HXmq2H8tl9tIiKKjubM/P4dX8uvj4W7nhnDV/ccwGdHN/v6w2Z/PXrrRSUVuBrtTCmRwzTLkjkvA7h9b6P1ppNB/M4Xmqv2ZZfWoFVKYL85L+kaH3yr064jY/VwmOX9eCBi7rWq845sX3AZrVw9+jO3DK8IwE2a01D9KgTpuC+tHfbmscp1oOMGdObGZd2541le9mdVUj3tiEMbB/OsE5RWBRsPpjP5sw8An2tZBeW8+P2I7y/Yl9Nz6i6An2t+NusBPv58K/J/RicGMnkmb9wy5uruaJfHIfzSpm/IZP+7cMZ3ime/JIKvtl4iK83ZNI21J9hnSLpFB1MWIAPc1Mz2XAgFx8L7NDbKSyr4NO1GfhYFFf1b8dvhnWgX0I4VXbN4q2H2Z1VRGyoP/42CwWllRSUVlBQWklUkC9Th3ao99tlFZSx+WAeBWWV2O2ahIgA4iMC8POx4m+z1MxUm1dcQdrRArq1DXE6HUl5pZ2NGbmkHq2kcusRhiRGEhZoo6LKzidrDtAtNoShSZGN/n1Lyquwa33aya2ssoqDx0tIig7ymLaiyio7lXZd81svS8ti+a5spl2QSFzYqUuSnk4SgXC7ptbpN+dqOcjPp15Dd119E8LomxBW8/zOkUlU2TWH8ko4eLyEwrJKquyannGhJDiqqep6a9oQHvkklfdWpFNZpbl7dGd+f2k3bI6R2P97RS8WbDzEsl3ZrNyTw1epmQAkRATw16t6893a7cz6aTe+VgvXDYrHboevUjOZs+YAfeJDKS6rYk92kdPYlQKt4Y3le7lxaAcO5ZWQeiCXzQfzG/09IgJtRAT6sjenCK1N9V6/hDD6xofRKTqIrMIydh4pZMXuHArLKs1B69cS6u/DHSOTWLTlCFsPmc+4cWh7zusQweaDefj6WOgTH2a6HJdUsHRnNgs3HaJKay7pGcvADuEUl1dRadcE+1kpr7SzJ6uIvJIKIoN8iQ7xIzrYj5zCMj5Zc4CconISIgK4om8cV/SLo298WL3f/2hBKdsPFXDgeDHZWZV0zC4iu7CMfTnFHCsqo6C0kphQfzpFBxHi74PVougQGUiII+mVVlTha7XUXFRU2TUKnPZ2259TzPT315JdWMZrNw2ivNLOne+spbzKztvL07nl/I48dll3p/+O7XZNWZVu9G/SFJVVdvZkFxFgs9I+MvCM3+9ESuszD7I1DR48WK9du7ZZx6akpDBmzJiWDaiFSYwto7VjtNv1KbvMllfaySkqo02wHz5WCykpKST2GUKgr5WYUH/AVBHN+/Ugn6w5gI/VwvQLOzG6extyCssorbATGuBDiL+NIF8rK3bn8PQ3W9l+uIBQfx96xIUyqms0Q5OiiAi0oZTiwPFiMnNLqKi0U1xRRcbxErILyujdLoyecSFsOpjHit05bD9cQGFZZc0Jc3inSEZ3iyFz1xZ69h3Af5fuJmVHFm1C/Hjqyl41q+fZNQT5Wqmwa8ora0tSwX4+TOwXh7/NyvwNmfV6lVVrG+pPRJAvx4rKyCksp9KusSgY1zOWEZ2j+GlnFsvSsqm0azpGBTJ9VCcu6RnL84t3MmfNgdP+GyllqipLK6o4mFtCZKAvF3SJpqisklV7crBYFAPah+NrtbArq9AkyfgwkndkARAeaOPg8RJ8rIrEqCCeu74/768wo/B7xYXy+OU9WLknh3XpxymtrKKwtJIMx29/ae9YJg2IZ9fRQnZnFXJh1zaM6hrN1xsP8e2mQ/SJD+PinrHklZiSGpiS8P6cYrYeymfHkQLKK+38bnQnnry8edPOK6XWaa0HO33NlYlAKTUeeAmwAm9orZ894XXleH0CUAxM01qvb+w9JRG4n8TYMloixiq7Jq+koubE31xaa7IKyggP9K03F1XdGHcdLSQm1K+mKunAsWLKKu10ig6iSmt2HS2kuLyKsAAfEiICa66QK6vsFJZVEuTng1UpiiuqsCjqLahkd3wPoN4gxdzichZtPcJHq/aTeiAXAB+LYtoFiYzrGUuHqEAWJP9CRPtutAnxIzEqiKhgX4J8fThSUMre7CJKyqsor7Sz80ghmw7mEuDrQ1J0EBnHilm+K5sgPx8u6ByFBn7dn4vdrukSE0xZpZ3UA7nERwTw8tQBRAT58thnG9iXU8x7dw4lJsQk7+TtR3nk01RyiyuwOpJJiL8Pgb5W2kcEkr5vP6uyFLnFFSgFkYG+5NRJjN1jQ0zbliORVpf2ACKDfOkZF0KvuFB6xoUyqGMEHaOCmvU3biwRuKxqSCllBV4FLgEygDVKqfla6611drsc6Oq4DQNmOu6FEE1gtSgiW2B0t1KqplTSkBPHmNStorCg6BkX6vQ4H6uF8MDaGIOdVPFZLMrpKPXwQF+mDG7P9YMSWJaWTfKOo9wwpD092tZ+VrcIK2MGtz/p2LiwgHr195f3beTLNdF/bxmMPmF6lbE9Yvj2oQtZt+84F3SOPunvkZJyhJfuupCNGXl0iw0mLMDGun3HWb4rm7HdY+jfPpzCskpW780hJsSfLjHB+FotlFXa8bdZWqWdxJVtBEOBXVrrPQBKqTnAJKBuIpgEvKdNsWSlUipcKRWntT7kwriEEGcZpRSjurU5qYOAu2I5UVxYABP7Ndxo7G+z1mtcH5wYyeDE2ufBfj5c1CO23jEBvq03HsZlVUNKqcnAeK31XY7ntwDDtNb319nnG+BZrfVyx/MlwONa67UnvNd0YDpAbGzsoDlz5jQrpsLCQoKDTx4560kkxpYhMbYMifHMeUp8Y8eObf2qIcBZeebErNOUfdBavw68DqaNoLn1qt5Sb+xqEmPLkBhbhqfH6OnxAbhyxZEMoG7FXQKQ2Yx9hBBCuJArE8EaoKtSKkkp5QtMBeafsM984FZlDAfypH1ACCFal8uqhrTWlUqp+4HvMd1H39Jab1FK3e14fRawENN1dBem++jtropHCCGEcy4dWay1Xog52dfdNqvOYw3c58oYhBBCNE5WJRdCCC8niUAIIbzcWTfXkFIqC9jXzMOjgewWDMcVJMaWITG2DInxzHlKfB211k5H5J11ieBMKKXWNjSgwlNIjC1DYmwZEuOZ8/T4QKqGhBDC60kiEEIIL+dtieB1dwfQBBJjy5AYW4bEeOY8PT7vaiMQQghxMm8rEQghhDiBJAIhhPByXpMIlFLjlVI7lFK7lFJPuDseAKVUe6VUslJqm1Jqi1LqIcf2SKXUYqVUmuM+ws1xWpVSvzrWj/DE+MKVUp8rpbY7fsvzPTDGRxx/481KqY+VUv7ujlEp9ZZS6qhSanOdbQ3GpJR60vH/Z4dS6jI3xvgvx996o1JqrlIq3NNirPPaDKWUVkpFuzPGU/GKRFBn2czLgV7AjUqpXu6NCoBK4Pda657AcOA+R1xPAEu01l2BJY7n7vQQsK3Oc0+L7yXgO611D6A/JlaPiVEpFQ88CAzWWvfBTMI41QNifAcYf8I2pzE5/l1OBXo7jnnN8f/KHTEuBvporfsBO4EnPTBGlFLtMUv17q+zzV0xNsorEgF1ls3UWpcD1ctmupXW+pDWer3jcQHmBBaPie1dx27vAle7JUBAKZUAXAG8UWezJ8UXCowC3gTQWpdrrXPxoBgdfIAApZQPEIhZd8OtMWqtlwLHTtjcUEyTgDla6zKt9V7MjMFD3RGj1nqR1rrS8XQlZh0Tj4rR4QXgf6i/2JZbYjwVb0kE8cCBOs8zHNs8hlIqERgIrAJiq9dlcNzHuDG0FzH/mO11tnlSfJ2ALOBtR/XVG0qpIE+KUWt9EHgOc2V4CLPuxiJPirGOhmLy1P9DdwDfOh57TIxKqauAg1rrDSe85DEx1uUtiaBJS2K6i1IqGPgCeFhrne/ueKoppSYCR7XW69wdSyN8gPOAmVrrgUAR7q+qqsdRzz4JSALaAUFKqZvdG9Vp87j/Q0qpP2KqVz+s3uRkt1aPUSkVCPwR+LOzl51sc/u5yFsSgccuiamUsmGSwIda6y8dm48opeIcr8cBR90U3gjgKqVUOqY67SKl1AceFB+Yv22G1nqV4/nnmMTgSTFeDOzVWmdprSuAL4ELPCzGag3F5FH/h5RStwETgZt07WAoT4mxMybpb3D830kA1iul2uI5MdbjLYmgKctmtjqllMLUbW/TWj9f56X5wG2Ox7cBX7V2bABa6ye11gla60TMb/aj1vpmT4kPQGt9GDiglOru2DQO2IoHxYipEhqulAp0/M3HYdqDPCnGag3FNB+YqpTyU0olAV2B1W6ID6XUeOBx4CqtdXGdlzwiRq31Jq11jNY60fF/JwM4z/Fv1SNiPInW2itumCUxdwK7gT+6Ox5HTCMxxcKNQKrjNgGIwvTYSHPcR3pArGOAbxyPPSo+YACw1vE7zgMiPDDGvwLbgc3A+4Cfu2MEPsa0WVRgTlZ3NhYTprpjN7ADuNyNMe7C1LNX/5+Z5WkxnvB6OhDtzhhPdZMpJoQQwst5S9WQEEKIBkgiEEIILyeJQAghvJwkAiGE8HKSCIQQwstJIhDiBEqpKqVUap1bi41UVkolOpulUgh38nF3AEJ4oBKt9QB3ByFEa5ESgRBNpJRKV0r9Qym12nHr4tjeUSm1xDE//hKlVAfH9ljHfPkbHLcLHG9lVUrNdqxPsEgpFeC2LyUEkgiEcCbghKqhG+q8lq+1Hgq8gpmZFcfj97SZH/9D4GXH9peBn7TW/THzH21xbO8KvKq17g3kAte59NsIcQoysliIEyilCrXWwU62pwMXaa33OCYLPKy1jlJKZQNxWusKx/ZDWutopVQWkKC1LqvzHonAYm0WfkEp9Thg01o/0wpfTQinpEQgxOnRDTxuaB9nyuo8rkLa6oSbSSIQ4vTcUOd+hePxL5jZWQFuApY7Hi8B7oGadZ9DWytIIU6HXIkIcbIApVRqneffaa2ru5D6KaVWYS6ibnRsexB4Syn1GGa1tNsd2x8CXldK3Ym58r8HM0ulEB5F2giEaCJHG8FgrXW2u2MRoiVJ1ZAQQng5KREIIYSXkxKBEEJ4OUkEQgjh5SQRCCGEl5NEIIQQXk4SgRBCeLn/D2GJju9mkoBlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 15;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\n\\n\\nmetrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\\naggreg_metrics = []\\nagg_col = \\\"epoch\\\"\\nfor i, dfg in metrics.groupby(agg_col):\\n    agg = dict(dfg.mean())\\n    agg[agg_col] = i\\n    aggreg_metrics.append(agg)\\n\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\ndf_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"Loss\\\"\\n)\\ndf_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"ACC\\\"\\n)\\n\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\n\\n\\nmetrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\\naggreg_metrics = []\\nagg_col = \\\"epoch\\\"\\nfor i, dfg in metrics.groupby(agg_col):\\n    agg = dict(dfg.mean())\\n    agg[agg_col] = i\\n    aggreg_metrics.append(agg)\\n\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\ndf_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"Loss\\\"\\n)\\ndf_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"ACC\\\"\\n)\\n\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/logger_csv_plot_basic.py\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "metrics = pd.read_csv(f\"{trainer.logger.log_dir}/metrics.csv\")\n",
    "\n",
    "aggreg_metrics = []\n",
    "agg_col = \"epoch\"\n",
    "for i, dfg in metrics.groupby(agg_col):\n",
    "    agg = dict(dfg.mean())\n",
    "    agg[agg_col] = i\n",
    "    aggreg_metrics.append(agg)\n",
    "\n",
    "df_metrics = pd.DataFrame(aggreg_metrics)\n",
    "df_metrics[[\"train_loss\", \"valid_loss\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"Loss\"\n",
    ")\n",
    "df_metrics[[\"train_acc\", \"valid_acc\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"ACC\"\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d4b55af",
   "metadata": {},
   "source": [
    "- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c4707995",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Restoring states from the checkpoint path at logs/my-model/version_9/checkpoints/epoch=148-step=26074.ckpt\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "Loaded model weights from checkpoint at logs/my-model/version_9/checkpoints/epoch=148-step=26074.ckpt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "36b288408b8e41ab8641ab59e37972b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "DATALOADER:0 TEST RESULTS\n",
      "{'test_acc': 0.7702000141143799}\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'test_acc': 0.7702000141143799}]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 16;\n",
       "                var nbb_unformatted_code = \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\";\n",
       "                var nbb_formatted_code = \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path=\\\"best\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93a80de6",
   "metadata": {},
   "source": [
    "## Predicting labels of new data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06be60cf",
   "metadata": {},
   "source": [
    "- We can use the `trainer.predict` method either on a new `DataLoader` (`trainer.predict(dataloaders=...)`) or `DataModule` (`trainer.predict(datamodule=...)`) to apply the model to new data.\n",
    "- Alternatively, we can also manually load the best model from a checkpoint as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4493c4b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logs/my-model/version_9/checkpoints/epoch=148-step=26074.ckpt\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 17;\n",
       "                var nbb_unformatted_code = \"path = trainer.checkpoint_callback.best_model_path\\nprint(path)\";\n",
       "                var nbb_formatted_code = \"path = trainer.checkpoint_callback.best_model_path\\nprint(path)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = trainer.checkpoint_callback.best_model_path\n",
    "print(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0523a8f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 18;\n",
       "                var nbb_unformatted_code = \"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\nlightning_model.eval();\";\n",
       "                var nbb_formatted_code = \"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\nlightning_model.eval()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\n",
    "lightning_model.eval();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0531cb79",
   "metadata": {},
   "source": [
    "- For simplicity, we reused our existing `pytorch_model` above. However, we could also reinitialize the `pytorch_model`, and the `.load_from_checkpoint` method would load the corresponding model weights for us from the checkpoint file.\n",
    "- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f199891f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([7, 3, 8, 0, 8])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 19;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/datamodule_testloader.py\\ntest_dataloader = data_module.test_dataloader()\\nacc = torchmetrics.Accuracy()\\n\\nfor batch in test_dataloader:\\n    features, true_labels = batch\\n\\n    with torch.no_grad():\\n        logits = lightning_model(features)\\n\\n    predicted_labels = torch.argmax(logits, dim=1)\\n    acc(predicted_labels, true_labels)\\n\\npredicted_labels[:5]\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/datamodule_testloader.py\\ntest_dataloader = data_module.test_dataloader()\\nacc = torchmetrics.Accuracy()\\n\\nfor batch in test_dataloader:\\n    features, true_labels = batch\\n\\n    with torch.no_grad():\\n        logits = lightning_model(features)\\n\\n    predicted_labels = torch.argmax(logits, dim=1)\\n    acc(predicted_labels, true_labels)\\n\\npredicted_labels[:5]\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/datamodule_testloader.py\n",
    "test_dataloader = data_module.test_dataloader()\n",
    "acc = torchmetrics.Accuracy()\n",
    "\n",
    "for batch in test_dataloader:\n",
    "    features, true_labels = batch\n",
    "\n",
    "    with torch.no_grad():\n",
    "        logits = lightning_model(features)\n",
    "\n",
    "    predicted_labels = torch.argmax(logits, dim=1)\n",
    "    acc(predicted_labels, true_labels)\n",
    "\n",
    "predicted_labels[:5]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e869d20",
   "metadata": {},
   "source": [
    "- As an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0f7e604e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.7702 (77.02%)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 20;\n",
       "                var nbb_unformatted_code = \"test_acc = acc.compute()\\nprint(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\";\n",
       "                var nbb_formatted_code = \"test_acc = acc.compute()\\nprint(f\\\"Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_acc = acc.compute()\n",
    "print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27c05abe",
   "metadata": {},
   "source": [
    "## Inspecting Failure Cases"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8fe7945",
   "metadata": {},
   "source": [
    "- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\n",
    "- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dbb00eb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 21;\n",
       "                var nbb_unformatted_code = \"class_dict = {0: 'airplane',\\n              1: 'automobile',\\n              2: 'bird',\\n              3: 'cat',\\n              4: 'deer',\\n              5: 'dog',\\n              6: 'frog',\\n              7: 'horse',\\n              8: 'ship',\\n              9: 'truck'}\";\n",
       "                var nbb_formatted_code = \"class_dict = {\\n    0: \\\"airplane\\\",\\n    1: \\\"automobile\\\",\\n    2: \\\"bird\\\",\\n    3: \\\"cat\\\",\\n    4: \\\"deer\\\",\\n    5: \\\"dog\\\",\\n    6: \\\"frog\\\",\\n    7: \\\"horse\\\",\\n    8: \\\"ship\\\",\\n    9: \\\"truck\\\",\\n}\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_dict = {0: 'airplane',\n",
    "              1: 'automobile',\n",
    "              2: 'bird',\n",
    "              3: 'cat',\n",
    "              4: 'deer',\n",
    "              5: 'dog',\n",
    "              6: 'frog',\n",
    "              7: 'horse',\n",
    "              8: 'ship',\n",
    "              9: 'truck'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cce50985",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 22;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/plot_failurecases_basic.py\\n# Append the folder that contains the\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n# files so we can import from them\\n\\nimport sys\\n\\nsys.path.append(\\\"../../pytorch_ipynb\\\")\\n\\nfrom helper_plotting import show_examples\\n\\n\\nshow_examples(\\n    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\n)\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/plot_failurecases_basic.py\\n# Append the folder that contains the\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n# files so we can import from them\\n\\nimport sys\\n\\nsys.path.append(\\\"../../pytorch_ipynb\\\")\\n\\nfrom helper_plotting import show_examples\\n\\n\\nshow_examples(model=lightning_model, data_loader=test_dataloader, class_dict=class_dict)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/plot_failurecases_basic.py\n",
    "# Append the folder that contains the\n",
    "# helper_data.py, helper_plotting.py, and helper_evaluate.py\n",
    "# files so we can import from them\n",
    "\n",
    "import sys\n",
    "\n",
    "sys.path.append(\"../../pytorch_ipynb\")\n",
    "\n",
    "from helper_plotting import show_examples\n",
    "\n",
    "\n",
    "show_examples(\n",
    "    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aab81c6",
   "metadata": {},
   "source": [
    "- In addition to inspecting failure cases visually, it is also informative to look at which classes the model confuses the most via a confusion matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f66cc607",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAATQAAAErCAYAAABQAX/RAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAB5m0lEQVR4nO2dZ3gUVRuG77ObHgJJIAm9hd57713pHaUI2JGmSBOUohQFP1FEUAERKUqXJkjvvffea4AkENJ3z/djJmGJKbubXUs493Xtld3ZmWfOTGbePW3eR0gpUSgUioyA4Z8ugEKhUDgKFdAUCkWGQQU0hUKRYVABTaFQZBhUQFMoFBkGFdAUCkWGweWfLkBGQ7h4SuGe2eG6+fIFOVwT4EmMyeGani7O+Z30cJKui8E5us6aEhVndrzuw6exDtcEcHMxOkU35PKpB1LKgKTLVUBzMMI9M+7FX3G47pgZgxyuCbDtUpjDNUtm93a4JkDRrM7Rzebp7hTdGJPZKbr3IqMdrvnzvpsO1wTImy2TU3Sndyh5LbnlqsmpUCgyDCqgKRSKDIMKaAqFIsOgAppCocgwqEEBJ9Lv1br0bF0NCZy6eIe3xiygee2SjHirGcUKBFH7ta84fOYGAF2aVWRg9waJ25YunIPq3b7k+Plbqe5j5tgPObpzE5n9sjL+t40A/Pr1OI7u2IjR1ZXA3Pl445PJePtksbrc8bExrB3TC1NcLNJsIn/VRlTo+B4Ap9ct4PT6hRiMLuQpX5vKXT+wWvfXz4dyZs9mMvlmZfCcdQDMHdOPkOtXAIiKeIxnpswMmrXaas3kWDH3e/5cNh+EIH/h4gz8dApu7h7p0gRoU7cM3t6ZMBiNGI0uzFmxJd2aTx6HM2nkAK6cPwNCMHT8VEqVr2yzznejP+Dw9o1k8c/Gl0s2AxARHspXQ98l5PYNAnLm4f0vZpAps6/N2ps/aoOLhxfCYEAYjNT66GfCb5zn5IKJmONiEQYjpV4Zgm+BklbpxcfG8PsnPTDFxWI2mShYvQlVOvfl0u71HFg0jdBbl2k/4VcCC5Wyuaz/qYAmhFgLvCqlDLNhmznAainlEmeVKzlyBmShT+c6lO80keiYOOZNeI2OTSpw4OQ1ugz5iW8/6vTc+r+uO8Sv6w4BUDI4B4u/fD3NYAZQq0VHGnV6jR9GvZ+4rGTV2nR8byhGFxd+mzqe1XOm0bnfR1aX3ejqxksfz8TVwwtzfByrR71G7nK1MMXGcO3gFtp+sRSjqxtR4Q+t1gSo3Kw9tdp2Z+H4DxOX9Rg1NfH9yu/G4+HtY5NmUh7cu8OqBTP5bsV23D08mTjoTbb/sYJGbbqkSzeBafNW4euf1SFaAFPHDadK7YaM/WYOcbGxREdH2aVTr2UnmnXuxbSPByQuW/HTNEpXqUWb3n1ZMftbVvw0jW4DRtilX+2D73DL5Jv4+eyyqRRu/gaBpWpw/8Quziz7luqDplulZXR1o9Wo2bh6emOKj2PFyO7kLV8b/7yFaDr4a7Z/P8auMsJ/rMkppXw5aTATGv/K43AxGvB0d8VoNODp4cadkHDOXb3HhWv3U92uU9MKLPrzsFX7KFahKt5JfnVLV6uD0UX7rQouVYHQe3dtKrcQAlcPLwDMpnikKR4QnNmwiDKtX8fo6gaAZxbbbuzgslXw8vFN9jspJUe3rKF8wxY2aSaHKd5EbEw0pvh4YqKj8A/Mnm5NZ/A04jHHDuyheYduALi6ueGT2fqatCUlKlYjUxbf55Yd2Lqeui07AlC3ZUcObFmXrvI+hxDERz8FIC46Ag/fbDZsKnD11KbgmE3xmE3xCAR+uYPxy1UgXcX619bQhBArgDyAB/C1lPIHIcRVoBKQCfgD2AJUB9oIIU4B3wP1gVCgi5QyJInmJ0BLwBPYDbwtpZRCiK3APn1bX+B1KeUOIYQRmAjUA9yBaVLK760p/+2QcKbM28L51aOIiolj096zbNp3zqpj79CkPB0HzbRq3bTYsfI3qjRuafN2ZrOJlcO78PjudYo36UJg4TI8vnONe2cPcejXb3Bxc6dyt0EEBNveLEiOy8cP4OOXjYDc6bugswXloG3Pd+nVuCJuHh6Ur16PCjXqOaSMQgj692yHEIK2r/SkTZee6dK7feMavv5ZmTi8LxfPnqJoybL0GzEeTy/HzLcLf/gAvwBtQrZfQBCPH9lWo05EwL6v+4OAfLXbkrd2W0p0fJ/93wzgzNJvkGZJjSE/2iRpNplYMrQj4XevU6rpKwQVKWNf2ZLwr6zZ6PSWUlZEC2D9hRBJqwNFgblSyvJSymuAN3BYSlkB2AaMSkbzWyllZSllKbSgZlkdcJFSVgEGWmz7OhAupawMVAbeFEL85Y4TQrwlhDgohDgo47Umg6+PJy3qlqJ4q7EUbPYJ3p7udHmpYpoHXblkPiKjYzl9ybZaVXKsnD0Vg4sLNV5qa/O2BoORNp8vpvN3Gwi5dJLQGxcwm+KJffqElp/Np3LXD9gy5UOHzYY/smkV5RvaHniTEhEexr4t65i1bj9zNx0jJiqSLasc09vww2/rmLtyG1/NXsySeTM5sn9XuvRM8fFcOH2c1q/0YtaKrXh4erHgh68dUlZHUmPwj9QeMZcqfadwdesSHl44wvXtyyjRcSANJ6yiRMeBHP9lnE2aBqORTpOX0eP7zdy/eIKH1y84pKz/5oDWXwhxDNiLVlMrnOT7a1LKvRafzcBv+vt5QK1kNOsLIfYJIU4ADQDLXsxl+t9DQH79fROghxDiKFoNLmsy5UBK+YOUspKUspJw8QSgQZUiXL39iAdhT4k3mVmx5TjVyqRd++jYtDyL1lvX3EyNnasXc3TnJt759BuEEHbruHtnJkeJStw8ugvvrEHkq9wQIQQBhUojhIHoJ6HpLqspPp4TO9ZTrn7zdGsd3budoFx5yeKfDRdXV6o3epkzxw6kWxcgICgHAP5ZA6jbuAWnj6fv/xSQPScB2XNSomwlAOo2a8X508fTXc4EsmTNRmjIPQBCQ+6R2c6+Pw9f7Qkj98z+ZC9Xj7Arp7i5Zw3Zy9cHIEfFhoRfPWWXtrt3ZnKWrMKNIzvt2j4p/8qAJoSoBzQCqkspywJH0JqeljxNQ+a5qoMQwgP4DuggpSwN/JhEM0b/a+JZU1wA/aSU5fRXASnln9Ycw427YVQplQ9Pd1cA6lcuzLmr91LdRghBu4blWPznEWt2kSLHd29lzdzpDPxyFu4enjZvH/X4ETFPHwMQHxvN7RN7yZKzAPkqNeDOqf0AhN++ijk+Dg8fv3SVFeDCoV0E5g3GNzBHurUCcuTm3PFDREdFIqXk2L4d5Cnwl98gm4mKfMrTiCeJ7/fv3EzBwsXTpZk1IIiA7Lm4flmrnRzes538wUXTXdYEKtVtwrZViwHYtmoxles1tVkjPiYqsa8sPiaKkDP78MkVjLtvAI/OawH94bmDeAXmsVozKtzi+oqJ5ubxPfims+8sgX9rH1oWIFRKGSmEKAZUs2IbA9AB+BV4FUga8hOC1wMhRCZ93bTaIuuBd4UQm6WUcUKIIsAtKWVawZQDp66xfNMx9sz/kHiTmWPnbjJr2W5a1SvN/wa3J5tfJpZNeYvj52/Rqt8MAGpVCObW/TCu3rK+r+O7EX05e2gPEWGhDGxehbZvfcDqOdOIj41l0ntdAQguXZ6ewydYrRkV+oDt00cizSak2UyB6k3JW7Eupvg4ds74hGUftsXo4krtPp/ZVPv7ZewALh3dx9PwUMZ2qEnTXgOo2rwTRzavpnyD9Dc3AYqWqUDNxi0Y2KkJBhcjwcVK06xj93TrPnoQwtA+Wue9Kd5Ek1btqV63Ubp1B3w8kc8+fJu4uDhy5snHsAnf2qUzZVgfTh/aw5OwR7zTtCKd3vmQNr3e46uh77B5xUKy5cjFB19Y1f37HLGPH3FwxhAApNlEzspNCSxZHRd3T04t+h/SZMLo6k6ZrsOt1owMDWHztx9hNpuR0kyhGk3JX6kel/dtZOes8UQ9fsTaCX3Ilr8oLT62rW9O/BtNUoQQ7sAKIBdwDggARgNzeDYosFrvC0vYJgL4CngZCAc6SylDLKdtCCE+A7oAV4EbaM3W0fqgwIdSyoNCiGzAQSllfn309DO0gQQBhABtpJThKZXd4B0knfFw+vfq4XT1cLqOejgdpncoeUhKWSnp8n9lDU1KGQO8lMxX+fW/D4C/DK9JKT8GPk6yrKfF+5HAyGS2q2fx/kHCfqSUZuAj/aVQKP7l/Cv70BQKhcIeMkxAk1I6p26rUCj+M2SYgKZQKBQqoCkUigyDCmgKhSLD8K8c5fwvkydPEEO/Gehw3bff/NzhmgChB+yb95QaZ249drgmQPFcjjefAeeV93FsnFN0q+V1XLaPBPbdcM45uHD3iVN0U0LV0BQKRYZBBTSFQpFhUAFNoVBkGFRAUygUGQYV0BQKRYZBBTSFQpFhUNM2/iZ+GT+Ek7s24+OXlZHz1gOwZtYUdq38lUy+/gC0enswpWrUT1OrX9f69GxbAyklpy7e5q1R8yiaP4ipI7rg7u5KvMnMwPG/cfDUNQBKFc7JtyNfwcfbA7NZUqvbF8TExltd9rff6M0fa1cTEBjIoaMn7Tj6lDGZTHRrWZeA7Dn5ZvaidOv9l8p648pFxn3wZuLnuzev0aPfUNr1eDtdurdv3eD9Pq8Tcu8ewmDg1dde5/W3+9ql9TjkDuumDONp6AOEEJRp2okKrXoAcHj1PI6umY/BYKRApbrU7TXYat0Nw1vj4q47SRmN1B0xN/G7i3/O4/SSb2j65Z+4p+BBkRL/moAmhGgDnJdSnnbiPvKTJO2QxXczgf9JKU8neBfomTccQrWX21O3fQ/mfvp8GqAGnXvT6NW3rNbJGZCFPq/UpXz7cZqb1Oe96di0Ip1fqsS4H/7gz12naVqrBOMGtqHpm19jNBqY/dlrvP7xXE6cv4V/Fm/i4k02lb37az15p09f3ujdw6btrGHhT9MpUKgoERGOma/0XyprngKFmLFcs8IzmUy8Wq8MNRu+nG5do9GFkWM/p3TZ8kQ8eULzhtWpXbchRYrZnpDSYDRSt/cQgoJLEhv5lHkftCdfuRo8DXvIpX2b6PHN77i4uhEZZrtfQY1B0/8SsKIe3SPk9D48/e0ztvk3NTnbACX+qZ1LKd9wZjAtXO6v7kz24mI0/sVNSkrI7K3lsMySyZM7IVrKtkbVi3Hywi1O6JZ4j8KfYjbblgOvVu06+Pv7O6Tslty7c4sdm9fTpovjgs9/qayWHNm7nRx58xOUy/rMrykRlD0HpcuWByCTjw+FChfj7p20LRGTI5N/IEHBWqZ6Ny9v/HMH8+ThPY798StV2r+Ji+4A5uXrmMm+Jxd9RYn2/cDOtPFODWhCiBVCiENCiFNCiLf0ZREW33cQQswRQtQAWgGThBBHhRDBQohyQoi9QojjQojlQgg/fZutQoivhBDbhRBnhBCVhRDLhBAX9ASOCdofCCFO6q+BFsVyEUL8rOsuEUJ4Wej+JWGcEKKbEGK/Xq7vdScoh7Ft6VzG9WjGL+OHEPk4xbyRidwOCWfK3E2c/+NTrmwYx+OIKDbtPcvgyUsYP7ANF/74lAnvt+WTqb8DUDhvIFLCymnvsXvBUD54Lf1ZVh3F5LHDGDB8LIZ/pwvhczi7rNvWrqD+y+0crnvj+lVOnThK+YpV0q0Vfu8W9y+fIUfRsoTevsrN04eY/2FnfhvenbsXTtikJYC9U/qx7bMeXN2+HIC7R7fj4RtAljxF7C6js6+ktJybAJBS7gZWAoP13P2XgLnAUCllGeAEz7s4xUop6wAzgN+B99ASPvYUQmQVQlQEegFV0dJ3vymEKK9vWxT4Qdd9DPRJqfBCiOJAZ6CmlLIcmt9A12TWS3R9irCh6l27bVfGLNrG8DlryZI1gKXfpu2c4+vjSYt6pSneYhQFm4zA29ONLi9X5q2OtRny5TIKv/QxQyYvZfoorZguRiM1yhek14g5NOz9P1o1KEu9KvZfMI5i+6Z1+GcNoETp8mmv/A/j7LLGxcayZ8t66jR1TBryBJ5GRPB2z1cYNW4yPpnT99hYbNRTVk7sT/03huHulQmzKZ6YiMe8OulX6vQazKrP37fJAazW0JnU/fgXqvWfwtWti3l4/jDn1/5EsVbp6z90dkBLy7kpWYQQWQBfKeU2fdHPQB2LVVbqf08Ap6SUd/Qst5f1/dQClkspn0opI9AcnWrr29yQUib4j6XkDpVAQ6AicEB3fmoIFEy6kqXrUyYbqt6Z/QMwGI0YDAZqtnqFa6ePpblNg6rFuHr7IQ9CI4iPN7Ni8zGqlS1A1xZVWbHpKABLNxyhUsl8ANy6H8aOQxd5GPaUqOg41u08Rfli6W/WpJdjB/eybeMfNK9ZmuH9enNw93ZGDHwz7Q3/AZxd1gM7NlGoRGn8sgU6TDMuLo63e3ahbYcuvNSyTbq0TPFxrJw4gOJ1W1K4RhMAfLJmp3D1xgghyFGkDMJgIOqx9Q5gSZ2kHpw/QuTD22z9tCsbhrcmOvQ+2z/rTnS4bd3YTgtoqTg3WYbxpE5O1pLg0GS2eJ/w2QWtRpsSSX9GUvtZEcDPFq5PRaWUo20tbEqEP3jmoH5s23pyFky75nTj7iOqlC6Ap4fuJlWlKOeu3ONOSDi1K2q/F/WqFOHidc1jecPu05QqnAtPD63PrXbFQpy5nH7Pz/TSb+ho1u09w5pdJ5gwdTaVatRh3BTbDDH+Lpxd1i1rlzu0uSmlZHD/tylUpBhv9hmQbq0/p44ka+6CVGrTM3F5oWoNuX5cc5F8dOsKpvg4PDNb5wD2Fyep0/vwy1+CZl+up/GE32k84Xc8/AKpM/IXPLJY78gOzh3lTMm56Z7elDsHtAUShoyeAD4AUspwIUSoEKK2lHIH0B3NPNhatgNzhBAT0YJSW10DIK8QorqUcg/wCn91h7JkE/C7EOIrKeV9IYQ/4KMbG9vE7FH9uXBkLxFhoYxoU53mrw/k/JG93LpwBgRkzZ6bV4aMT1PnwMlrLN94hD0LhmpuUmdvMmvpLo6dvcGkwR1wcTEQExNP388WAhD2JIpv5m1m57whSClZv/MU63ba5qHYo9sr7Ni2lQcPHhCcPzcffzKGnr1ft/UU/C38l8oKEB0VyeHd2xg4erLDNA/s282yRQsoVqIUzepqfWdDRo6lQeNmNmvdOnOY01tWki1fEeYO0Ayra3UfSKlG7Vj/zUjm9G2J0cWVlwZMsNoBLObxIw5M16Z4SJOJXFWaEliqus1lSw6nuT6l4tyUDfgczXXpJJBJStlTCFETzSszBs1izgetj8wLrSnZS0oZmsShqZ7+voW+T8vvPgB668WZKaWcok/bWIsW8GoAF4DuetC13PYq+rQNIURnYDhabTYOeC+JwfFz5CtWRg6dvTKlr+1m0HuOu+AtUemDnFfeB5Exaa9kB8WCHH8evt51xeGa4Lz0QSvfqvL3uj6l4twEyfhh6v1aSadt/MWPM4lD01Zgawrf/Q/4X5Jtryazj+S2zW/x/jeeObIrFIp/Mf/+8XKFQqGwEhXQFApFhkEFNIVCkWFQAU2hUGQYVEBTKBQZhn9Nto2MQnh0HOtPhThcd/1vYx2uCeBXZ7jDNZu/0cHhmgBPY6xPeWQLnm7OuQ1cjPY9YJ0WwYHeDtesmsc5U2Jq5cviFN2UJkapGppCocgwqICmUCgyDCqgKRSKDIMKaAqFIsOgAppCocgwqICmUCgyDGraxt+Is5xuLHnyOJxJIwdw5fwZEIKh46dSqnxlq7bNksmD6cPbUaJgEFLCO+OXEhkdy9QhbfH2dOPanVB6jf6NJ3oWiQ+716Vny8qYTGYGTVnFxn0X0tyHNJvYNLY7nn6B1BwwhbAb5zkydwLxMZF4ZctJlTc/xdUzk83HHRf5hJPzxxNx+zIApbqPJCbsPhfXzCTi7lWqD5lNlny2mYSsH9oSFw8vhMGIMBip//Ev3Dq4kTMrf+DJnSvUG/Ezfvnts8GQZhMbx3TH0y+AWgO/BuDCxl+5uGkRBoORHGVrUaaT9bnMHofcYfWXQzV3JoOBss06Ubn1M/+DfUtnsWX2JPov2INXFuvylgF8N/oDDm/fSBb/bHy5ZDMAEeGhfDX0XUJu3yAgZx7e/2IGmdLhl7Fq/kw2LJ0PUtK4fVdadrM/eeYLFdBScn2ydHxKY/ueaGmF7PMEwzlON5ZMHTecKrUbMvabOcTFxhIdHWX1tpMHtuTPved5dcQCXF2MeHm4subr1xk2dS07j16hR/OKvN+1DmN/3ECx/IF0bFSWCl2/Ike2zKz95nVKd/4yTQOWCxsWkjlnAeKitAR/h+d8RulOAwgoWpGrO37n/LpfKNn2XZuP+8zir8hWohrl35yAOT4OU2w0rp6ZKPfWRE4tmGizXgK1Pvz+uf+XT85gqvb5gqNz085dlxoXNizEJ0f+xESH988c4PaRbTQZ+ytGVzeiHz+ySc9gNNLgjaFkL1SSmMgI5gxoT4HyNciWtxCPQ+5w9ehuMgfktLmc9Vp2olnnXkz7+FlwXfHTNEpXqUWb3n1ZMftbVvw0jW4DRtisDXDtwlk2LJ3PpPlrcHF1Y2yfV6lYuyE58/0lMbRVqCYnKTs+OdoQJSXS63STwNOIxxw7sIfmHboB4Ormhk9m6yY2+ni5U6tcfuasOghAXLyJ8IhoCufNxs6jWq6szQcu0qae5gDUonZxFm88RmyciWt3Qrl08yGVS6Se2jvy0T3uHt9F/tptEpc9uXuNbEUqABBYsiq3Dm226ZgB4qOeEnrxCLlrtALA4OKKq5cPmXIUIFNQPpv1UiNzzgL4ZM+fLo3IR/e4c2wnBeu0SVx2acsSir3cE6PuouSR2Tbnqkz+gWQvpP1v3L0ykTWP5s4EsOnHCdTrNTj1PM4pUKJiNTJl8X1u2YGt66nbsiMAdVt25MCWdbYL69y8coGiZSrg7umF0cWFkhWrs2/zH3brvYgB7S+uT5aOT0KICCHEWCHEPqC6EKKXEOK8EGIbUDM9O3aW000Ct29cw9c/KxOH9+X1NvX4YsQAoiKfWrVtgVz+PAh7yg8jOrBnTj++G9YOLw9XTl++R4vaWlOtXYPS5A70BSBXQBZu3n/mUnXrfjg5A1KfbX781y8p3bH/c5lNM+cK5s5RLRnxzQMbiXp0z5ZDBiDywS3cMvlx4pdP2TW+ByfnjSM+xvqaaYoIwa6v3mPL2G5c2bYs/Xo6Rxd+qTUnDc9uvyd3r/Pg/BE2fdqDLRPf5NFl27IKWxJ27yb3L58hZ9GyXNi7mUxZgwgqWMwRRQcg/OED/AKCAPALCOLxI9s9ORPIW6gYpw7t43HYI2KiIjm0czMP7t62W+9FDGhpuT55AyellFWBS8AYtEDWmBSSQ1q6PsU+CUtxx85yuknAFB/PhdPHaf1KL2at2IqHpxcLfvjaqm1djAbKFcnJj8v3Ub3nVCKjY/mwez3eHr+Ut9tXZ9fsvmTycic2waQ4mV/71LIf3zm2A3cff/zyP9+PVbHXJ1zavJhNY7sRHx2JwcXV6uNN3K/ZxOMb58hbux01P5qL0c2TK3/OTXvDNKgzbBYNPplPjYHfcHnLYh6cP5xuzdtHt+Ph4/eX8yDNJmIjH9Ng5M+U7TSAPdOH2eSilEBs1FOWj+tPwzeHYzAY2f3bDGp365/ucjuLPAUL065XH8a83YWxfbqSv0gJjC7294S9UH1oOkldn5L+t03AUv19VWCrlDIEQAjxG/CXqpSU8gfgBwDf/MVTvApTc7oBEp1uan/0k83mEAAB2XMSkD0nJcpqmYnrNmtldUC7dT+cWyGPOXD6BgDLt5xkUPe6jP1xAy0HzgagUJ5svFSjaOL6uQOfNWdzBWbhzoOU0y0/vHiMO8e2c/fELkxxscRHR7D/x4+p8uan1B40DdCan3dPpGbxkDwevoG4+wbgW0DrGg2q0IAr69Mf0Dwt/l85y9cj9MqpxOaxvTy8cIzbR7dz5/iz87Dv+5F4+gWSq2IDhBD4FyyFEILYJ2G4W2k8Apo70/Lx/SlZvyVFazbh/tVzhN+7yey+rQF48uAecwa0o8f/FpHJP8DuY8iSNRuhIffwCwgiNOQemf3TZzLcqN2rNGr3KgDzvplA1qAcdmu9iDW0tFyfoqWUplS+twtnOt0kkDUgiIDsubh+WRttPLxnO/mDi1q17b1HEdy8F0bhvNq+61UK5uyV+wT4aQ9CCyEY1rM+Py7fB8CanWfo2Kgsbq5G8uXwo1DubInBMDlKte/Ly5PX8tIXq6j69jgCilWmypufJnZ+S7OZs6tnUbBue5uP2z1LVjz9goi4p3nXPDx7AO8cBWzWsSQ+Joo4i//X/dP7yJwrOF2aAKU79qPF//6g+eTVVHt3PIHFK1P17c/IVaEe988cALTAbo6Px82G0W4pJWu/HknWPMFUadsLgMD8Rem/YDd9ftpMn58245MtiJ5fL0tXMAOoVLcJ21YtBmDbqsVUrtc0XXphDzWrupA7N9m7aS21X2pjt9aLWENLzvUpJYfXfcDXukHyY6AjkLZ5ZjI40+nGkgEfT+SzD98mLi6OnHnyMWyC9SYoH3y1ip9GdcbN1cjV2494a9wSur5UgbfbaeX8fdtJ5q45BMCZK/dZuvk4Rxa8T3y8mYFf/p7mCGdy3Ni3nstbtJsjZ4X65KvVymYNgOKdBnH8p1GY4+PwypaL0j1Gcu/oVk4v+pLYiDAOffcBPrmLULmfdTXWmMcP2TtN/3+ZTeSp0pSgUjW4fXgLxxZOIvZJKHu+HkiWvEWo+X76jWYK1G7NgVljWD+yEwajC1XeGG21ixLAzdOHObX5dwLyF2F23zYA1H3tfYIr101XuaYM68PpQ3t4EvaId5pWpNM7H9Km13t8NfQdNq9YSLYcufjgi+/TtY8vBr3Bk/BQXFxceeuj8emaAuI016d/Iym5PunLEhyfIqSUmSy26YXm+nQHOAoYU5u24Zu/uLScX+YoBtVNf+0gOZr2nORwTZU+SOO/lD6oQk4fh2sCuBqccw7als3597o+/RtJxfWpnsU6z83qlFL+BPzk1IIpFAqH8CL2oSkUigyKCmgKhSLDoAKaQqHIMKiAplAoMgwv1KDA30EmdxeqB9v2HJ413IuMdrgmQJ2urR2uuebXLQ7XBKjeooZTdE+euusU3QplbX8Y3BpuPnL8tRDgbfsTGtZwNTTGKbopoWpoCoUiw6ACmkKhyDCogKZQKDIMKqApFIoMgwpoCoUiw6ACmkKhyDCoaRt/E0smDePs3s1k8s3KwFlaiuE7l86w/KuPiY2OxC8oF50/+h8e3rY9JOwsE4u4yCecWjCBiDuXEAhKdh2Bb8HSAFzdOJ/zK76l3sQ/cMuUtu7ZuW/yJCoWk1kSbzJTq+88/Hw8+GVEC/IFZeHavXC6fbaKsIhnQ/x5Anw4PLMX437ZzZQlB1PVj7x3jVM/f5L4OerBbQq8/AYx4SE8PLkLYXTFM1suir36Ea5eKZ/fR5umEn3tIAbPLGR/5RsAYkMuE7ZtBjI+FgxG/Oq+jVtQEaQpjtCt04m9fxEhDGSp/ToeuUqneS4gebOcsyt/4PrO3xPPZ/G2fQgqbVuCZLPZxB+fvIqXXyD1B03l2r4/Ob58BuG3r/DS6HlkLVjSJj2AXz8fypk92nU7eI6WanvumH6EXNfSskdFPMYzU2YGzVptteaTB3fY8PVwIkMfIAyCko07Ua5ld0KunGXrjDHERUfiE5iLpu9/gZuXbYY5KqBZgRCiHhArpdxtr0bFpu2o3robiz8fnLhs6Zcf8fLbwyhYtioH/1jM9kUzadLrfZt0nWVicXaJZjpS7o3xiaYjANGh93h49gAefrYZujQbvIiHj5+lxf6wcxW2HrnO5N/282HnKnzYuSojZ21P/P6Ld+rz54ErVml7BeWj8pCfAS3Vz+5P2hBQpi6R969RsMU7GIwuXFr5Hdc3/kJwq6QJip/hXbwBmcq8zKONz1IMhe/5GZ/KnfHMV5GoqwcJ2/0zgW3H8fT0BgCyv/INpsgwHqwei3vHyQhhXaMnObOcgo1eoVCTblZtnxxn1y8gi4UBjW/uQtQZ8D/2zf7Ubs3KzdpTq213Fo7/MHFZj1FTE9+v/G68zT/CBoMLtXoOITC4BLFRT/ltUAfylqvO5u8+odZrg8lVqjKnNy7l8IrZVHvVtmy7qslpHfXQ0g3ZTYEyVfBKUkt6cOMyBcpUAaBQxZqc2m672YQzTCzio54SeukouapraeISTEcAzi79miJt3rPLcMOSFtULMW+Dljd/3oZTtKxRKPG7ljUKceVuOKev2Z6rPvT8QTyy5cLDPzv+xapiMGq/2ZnzlSQm7H6q27rnLInBPWmNQCBjtUAsYyMxemuTpuMe3cA9dxkAjF6+GNy8ibt/0ebyOoqnj+5x++gOCtVtl7gsS66CZMmRP126wWWr4JVCokkpJUe3rKF8wxY2aXr7BxAYrCW9cfP0xi93QSIe3if01hVyltQyAuUpV4OLe/60ubwvdEATQvTQzVKOCSF+EUK0FELsE0IcEUJsFEIE6TnU3gHeF0IcFULUdtT+g/IX4czujQCc2PYHYSGOmbGeXhOLyIe3cMvky6l5n7FnYg9OzR+vZW09vgMP3wB8che2SU8CqyZ0YNe0bvR+WQsCgX5e3H2k1STuPnpKgK8XAF4ergzqVIVxv9hXGb53eBNBFRr9ZfmdfWvwL257Qk3fWq8TvnsOd35+nbDdc8hSrTsArtnyE31lP9JsIv7xPWJDLhEf8cAqzeTMcgCubFnMljGvcmTOp8Q+fWxTOQ/Nm0T5LgPBSfnHkuPy8QP4+GUjILf92YEf379FyJUzZC9Shqx5C3Nlv9ZtcnHXeiIe2H4/vLBNTiFESWAEUFNK+UAI4Y9271WTUkohxBvAECnlICHEDCBCSjnZkWVoP3giq74dy6ZfvqV4jYYY7TAIcQbSZOLJjfMU6zgI3/wlObvkKy6tnUnoxaNU7GtdxldLGgxcwB09aK2e0IFzN1L2nPy4ew2mLjvE0+g4m/djjo/j4cmdBLd457nlV//8GWEwElSpic2aT0+uI0ut3ngF1yDywk5Ct3xLQOuxeBdvRHzoTe4vGoTRJxD37MWw1vWw1tCZePgGEPP4EXum9MUnez7y12tP0RavA4Kzv8/g1OKvKd/zY6v0bh7ZjkdmP7IWKMFdPY3338GRTaso3zClZM9pExv1lLWfD6B27+G4eWWiYd/P2D5zPPsXTadAlfp23Q8vbEADGgBLpJQPAKSUj4QQpYHfhBA5ADfAqk4cIcRbwFsAvoHWP78XmDeY17/Q+n5Cblzh3N6ttpQ/RdJrYuHhp5uO5Nc6kYPK1efS2plEPbzDnglaDSUmLIS9n/ek6uBZuGdOXf+OXhMLCYtk5e6LVC6anfuhkWT39+buo6dk9/cmJCwSgMrFctC2dhHGvVGHLJncMZsl0bEmZqw8kma5H57ZS6bcRXCz8LS8s38tD0/totx739iU0jqBp+e2kKX2GwB4FqpJ6BbN0EUYjPjWej1xvftLh+Lia93/PqlZTujV02S1MF/JV7sN+779wOoyhpw/ys3D27h1bCemuFjiop6yc/pH1Ho3fWbIqWGKj+fEjvW8//3vdm4fxx9fDKRonRYUqt4YAP/cBWkzeiYAobeucvXg9tQkkuVFDmiCvxqgTEVzUF+pDwSMtkbI0vUpd9HSVuc0jwh9SCa/rJjNZrbMn0bVlq9Yu2mqJJhYtOnd1y4TC/fMWfHwC+LpvWt4B+Xj4bmD+OQpSqX+z3Lnb/+kLdWG/JTmKKeXhysGARFRcXh5uNKoQj7Gz9/Dmr2X6Na4JJN/20+3xiVZvUfrf2o06NfEbUd0r8HTqFirghnA/UMbCKrQOPHzwzN7ub5xPuX7f4vRzcOGM/AMo7c/MbdP4pGrNDE3j+PiqzkSmeNiAInB1YPoG0dBGHH1T91oGTTDFaQZFw/vRLOcoi3eIDrsAR6+mkHNnSNb8clpfcr18p37U76z1nl+98wBzqyd69RgBnDh0C4C8wbjG2i7Q5OUkk3TPsYvd0HKt+6ZuDwy7CFevlmRZjMHlsygdNNONmu/yAFtE7BcCPGVlPKh3uTMAtzSv3/NYt0nQOouummw8LOBXDm2j6fhoUzoXJNGrw0gNiqSPb/PA6BU7SZUbGZ7Ln5nmVgU6/gBJ+aMxmyKwzNbLkp1s22UNIFAXy9+G6Vl9HAxGvhtyxk2HLzKoXN3mTeyJa81K82N+4/p+tkqu/QTMMVG8+jcAYp2HpK47MKS/2GOj+PYdwMBbWDA8vukPPzzS2JuncQc/Zg7c14nc5Uu+NXrQ9jOmYSbzWB0xa+eNkpqjgrjwaoxIAwYvf3xbzTQqnKmZJZzeNYowm+cByHwypqDst2G23ciLLh+cDMH504k+kkoW77sh1++ojQcMt0mjV/GDuDSUe26HduhJk17DaBq804c2bya8g3sa27eOXOYc1tXkjVfERa+3xaA6t0GEnb7Oif+WABAwWqNKd6wXWoyyfJCmaQkRQjxGjAYzYvzCLAc+AotqO0FKksp6wkhigBLADPQT0q5IyXN3EVLy77TVzi8rMH+ng7XBJi567rDNbf/bru3pjU4K33QhfMhTtF1VvqgzJ5uDtcsn8c5JinOSh80tW0JZZKSFCnlz8DPSRb/pVNASnkeKPO3FEqhUNjNCz1tQ6FQZCxUQFMoFBkGFdAUCkWGQQU0hUKRYVABTaFQZBhe6FFOZ2CWksg4k8N1SwdlcbgmgLeH4x+3atfjr89SOoJlU+c7RbfLwO5O0S0a5O0U3T2XUn50zF7yZLFv4nFavFwkyCm6U1NYnmJAE0I84dlM+oRnRqT+Xkop0zXRVKFQKBxNigFNSumcmXYKhULhJKzqQxNC1BJC9NLfZxNC2J8vRKFQKJxEmgFNCDEKGAokPFzmBsxzZqEUCoXCHqypobUFWgFPAaSUtwHVHFUoFP86rAlosVJ7gl0CCCGcM3SjUCgU6cSaaRuLhBDfA75CiDeB3sCPzi1WxiM85A6/TxpCROgDhDBQ4eVOVG3zGht//Jzz+7ZgdHHFL2deWn0wAY9Mtg8gx0RH071dU2JjY4iPj6dp8zb0GzzS7vKuH9oSFw8vhMGIMBip//Ev3Dq4kTMrf+DJnSvUG/EzfvlL2KxrNptY/0lXPP0CqTfoG44s/IpbR7ZjcHElU2Buqr05Brc0TDcK5w3gl3HPzEQK5PLn0x/WkyWTJ71bVyUkTEsoOWr6H6zffZYuTcszsFu9xPVLF8pO9R5fc/zC7RT3ER8bw7qxvTDFxyFN8eSr2pjyHfrw8OpZ9sz+DFNcLAaDkWq9PiKgkHVuT+A89y9wjpuUs1zFEnD0dZtmQJNSThZCNAYeA0WAT6SUG+ze4z+IEGI0TkilbQ0Gg5HGbw4jR+GSxERGMLNfewqWr0mBCjVp0HsQBqMLG2dNYudv39Po9cFpCybBzd2dnxavwds7E3FxcXRr05jaDZpQrmIVu8tc68Pvn3Mm8skZTNU+X3B0rv3JA8+tX0BmC2ei7KWqUbZTPwxGF478+jWnVs2mfJcBqWpcuB5Cte5fAWAwCC6t/piVW0/SvUVlpv66gynztz23/q/rj/Drei1JZMng7Cye1DPVYAZgdHWj6ciZuHp4YY6PY+2YnuQqW4ujS6ZRrt075C5Xi5tHdnBw4RRe+niW1cfvLPevBBztJuUsV7EEHH3dWvukwAlgB7Bdf//CIoSwazKyT9ZAchTWUlq7e2UiW56CPHl4j+CKtRKdiXIXK8cTO4wh9HLh7a05FsXHxREXF2dXyunUyJyzAD7Z89u9feSje9w+upPgum0Tl+UoXT3x+LMVKk3ko3s2adavXJgrNx9y/W6YVet3alKORX8eTXM9IQSuHppxi9kUj9kUj3Y6BXFREQDERkXg5RdgU3md5f7lLJzhKmaJo69ba0Y53wD2A+2ADsBeIURvu/f4NyOEGCGEOCeE2AgU1ZcFCyHWCSEOCSF2CCGK6csDhBBLhRAH9FdNffloIcQPQog/gbnpLVPY3ZvcvXSGXEXLPrf86J9LCa5Ux25dk8lE20bVqVWmADXqNKBshcr2F1IIdn31HlvGduPKtmX261igORMNQBiSv+wubfudnGVtM9ft2Lgsi/58lqL7nQ412D/vA2aM7Iivz1+TYnZoVO659VPDbDbx+/BO/PpOfXKWrkZAoTJU6TGEgwu+YlHfJhyc/yUVO9vmG5kcjnL/coabVHKk11UsKY68bq2poQ0Gykspe0opXwMqok3j+NcjhKgIdAHKowXkhDP1A1rm2YrAh8B3+vKvga+klJWB9sBMC7mKQGsp5avJ7OctIcRBIcTByPDQVMsUG/WUxZ/1p8nbH+Hu/cwDcsfC6RiMRko3aGXPoQJgNBpZvnEPWw6d48TRg5w/e8purTrDZtHgk/nUGPgNl7cs5sH5w3ZrAdw6sh2PzP74F0i+3+3k7zMxGI3kr/Gy1ZquLkaa1y7Jss3HAfhx2R5KtJ9I1e5fcffBEyYOeN4vsnLJPERGx3L6snW1QIPBSOsJi+j47Z88uHSS0BsXOLdxEZW7D6bTt39Suftgdv0w2urypkT7wRPZ8/s8pr7Tmpiop3a7f9UaOpO6H/9Ctf5TuLp1MQ/PHyZ/vfY0GreMeh/PwyNLVk4ttt21y9k48rq1JqDdRMupn8AT4Ibde/x7qQ0sl1JGSikfAysBDzTT4MVCiKPA90CC00Mj4Ft9+UogsxAioXd2pZQyimSQUv4gpawkpazklcUvxcKY4uNY/Gl/StdvSfFazyzVjm1YzoV9W2k7ZLJDmomZs/hSpXptdm7ZaLeGp4UzUc7y9Qi9Yv9FBs+ciX5//2V2TRvGvdMH2D1d63e5vGMlt45up8a742w6/qY1inH03C3uP9KagPcfRWA2S6SUzP59H5VK5H1u/Y6NrWtuJsXdOzPZi1fm1rHdXNy+inyVGwKQv2oTHlw+abNeUhLcv/rN+J2y9VuSNWfetDdKhuTcpDwyZ9UHdgzkq92GsKvp+z/CM1cxwC5XsZRwxHWbYkATQnwghPgALb/+Pr3ZNQot1/4/ZxFtO0lNEwxAmJSynMWruMV31S2W55JSJgTzp+kqhJSs+moE2fIWpFr7XonLLx7czu7FP9J59HRcPez3DXj0MITH4WEAREdFsWfHFgoUKmKXVnxMFHHRTxPf3z+9j8y5rHchSo5ynfvT9pv1tP5qLTXfm0hQicrUeHcct4/v4vTqOdR9fwou7rYdv9Yf9qz5mD3rs5HB1nVLcfrys6abEIJ2DcuweMNRq7SjHz8iRm+excdGc/vkXrLkzI+XXwB3zxwE4M6p/WQOsi/4WBIRqjXZ0uP+FR8TRbzF/yzk9D4y5wwmOuyZ+bGtblIpkeAqBtjlKmaJI69bSH2UM+HquKS/ErDPiO+fYTswRwgxEe1YW6LVyK4IITpKKRcLrUpQRkp5DPgT6AtMAhBClJNSHnVEQW6cOsSJTb8TmL8IP/TRXJDq9/yA9dO1KQDzP9KCXK5iZWnef6zN+iH37jF8wFuYzCbMZjPNWrajfuOX7CprzOOH7J2mOxOZTeSp0pSgUjW4fXgLxxZOIvZJKHu+HkiWvEWo+f63aailzsGfP8ccH8vmz98FtIGBKr3SHrb3dHelQZXC9J2wNHHZuH7NKVM4J1LCtTuP6Dfx2Xe1yhfg1v1wrt62LlNFZNgDdk4fiTSbkdJM/mpNyFOhLm5ePuyf+wVmswmjqxvV3/jEpuN1lvuXs9yknOUqloAjr1t4AVyfhBAjgB7ANbTm82lgKTAdranpCvwqpRwrhMgGTAOKowXA7VLKd2yZ7pGzSCn5xlTHdKJb8mqZXA7XBPho7VmHa3q6W+cgbisqfZCGM9IHdavsnOvLWWmviufMZJ/rkxAiABgClETrfwJAStnAoSV0ElLKccC4ZL5qlsy6D4DOySwf7fiSKRQKR2PNoMB84CxQABgDXAUOOLFMCoVCYRfWBLSsUspZQJyUcpuUsjdQzcnlUigUCpuxZtZ7nP73jhCiOXAbyO28IikUCoV9WBPQPhNCZAEGoaXyzgzY96CZQqFQOBFrHk5frb8NB+o7tzgKhUJhP6mZpEzlr5NSE5FSpv8htgyIu4uBQlntnyCbEpceRjhcEyCLl+Ndn/aftO9ZxLTo0M++jBFp8eu3vzlF983BzimvwcFJBwAK+WVKeyU72H/L8VNMUiO1GtrBv60UCoVC4QBSc336+e8siEKhUKQX5ZyuUCgyDCqgKRSKDIMKaAqFIsNgTcbaIkKITUKIk/rnMkII+10MFAqFwklYM7H2R7Sstd8DSCmPCyEWAJ85s2AZjZljP+Tozk1k9svK+N+0BHa/fj2Oozs2YnR1JTB3Pt74ZDLePvZnJ1gx93v+XDYfhCB/4eIM/HQKbu4eaW+YBEc6Ht1e/SVPLu7DxcuX4Ld+AODeph95cmEvwuiKm18OcrYYhNFDmzYQff8yd/74BnPMUxAGCvSaisHFLc0ym80m/vjkVbz8Aqk/aCrX9v3J8eUzCL99hZdGzyNrwZJWHXuWTB5MH9aWEgWDkFLyzvhl9O1cg8J5teSJvpk8CIuIplrPb3ExGpg+vC3liuTExWhg/rojTP5le6r6Tx7cYcPXw4kMfYAwCEo27kS5lt0JuXKWrTPGEBcdiU9gLpq+/wVuXrZNpVg/rJXmgyAMGIwu1Bs5l5OLv+bu8R0YjK54B+SmfK9PcPOy31bXZDLRrWVdArLn5JvZi+zWcdb9YE1A85JS7k+SSTTepr38S/k7XaBqtehIo06v8cOoZw9ZlKxam47vDcXo4sJvU8ezes40Ovf7yC79B/fusGrBTL5bsR13D08mDnqT7X+soFGbLjZrOdLxKEuZJvhVasXtlZMSl3kXqEBg/d4Ig5F7m2fyYPevBDV4A2k2cev3L8jVajAeQcHERz5GGKxLRXR2/QKyWLhJ+eYuRJ0B/2Pf7E9tOvbJA5vz574LvDpyIa4uRrw8XOn+ybN5ahP7vkT402gA2jcohburC5V7TMXT3ZUj8wewaMPxVA1bDAYXavUcQmBwCWKjnvLboA7kLVedzd99Qq3XBpOrVGVOb1zK4RWzqfaq7VM9aw6a8ZzrU2CJqpRo9x4Gowunlkzlwto5lOzQz2bdBBb+NJ0ChYoSEfEk7ZVTwVn3gzV9aA+EEME8MxruANyxaS8KilWoincSt5/S1epgdNF+U4JLVSD0XvompJriTcTGRGOKjycmOgr/wOx26TjS8cg7b2mMHs/XCDIVrJgYqDxzFSfuiZZVNeLyITwCC+ARpGVVdfHKbFVAe/roHreP7qBQ3XaJy7LkKkiWHPnT3NYSHy93apXNz5xV2hTMuHgT4RHRz63TvkEpFm3QPAykBC8PN4xGA57uLsTGmXjyNCbVfXj7BxAYrPkquHl645e7IBEP7xN66wo5S2rpvfKUq8HFPX/aVPaUCCxZLdFVy69gKaJCbXPVsuTenVvs2LyeNl16pLtczrofrKmhvYdmKlJMCHELuAI4Zwr034BFwscbQAhwSAhRDpgBeKFl5+0tpQwVQlQGZqGl394JvCSlLOWMcu1Y+RtVGre0e/tsQTlo2/NdejWuiJuHB+Wr16NCjXp265nNJlaNeIUnd69TrEnnRMejDRPf5cD8/4E08/LodBtgEXZsPZlL1AUg9tFNEIJrCz/CFBlO5hJ1yVa9U5oampvUwMS04fZSIJc/D8Ii+WFEe0oXys6Rc7f5cMpqIqO1/Aw1y+bnXuhTLt3UUmYv23KSFrWLc+X3YXh5uDLkm7WEPknWdiJZHt+/RciVM2QvUoaseQtzZf9mClZtyMVd64mww85QINg9pS8gKFC3LfnrtHvu+2u7VpK7cmObdROYPHYYA4aPJTLCOU+tWGLv/ZBmDU1KeVlK2QgIAIpJKWtJKa/aXsR/nlRcoOYCQ6WUZdB8R0fpy38C3pFSVgdMqegmuj49CbX9UY+Vs6dicHGhxktt0145BSLCw9i3ZR2z1u1n7qZjxERFsmXVErv1/g7Ho5BdCxAGI1lKarlCpdlE5I2T5Go9lPw9vuTJ+d1EXEndcu7mke14ZPYjawpuUrbgYjRQrkgOfly+j+q9phEZFcuH3esmft+pcRkWbziW+LlyidyYzGYKtp5I8Q6TGfBKTfLnTNkkx5LYqKes/XwAtXsPx80rEw37fsbxPxby66AOxEbb5/xUe9hM6n88jxoDvubyliXPOXWdWzMbg8GF3FXtS2+9fdM6/LMGUKJ0ebu2t4X03A/WjHJ+IoT4BC3bxvsWn/+LJOcC5Q34SikT7LZ/BuoIIXwBHynlbn35gpRELV2ffPz8bSrQztWLObpzE+98+k26HJ+O7t1OUK68ZPHPhourK9UbvcyZY+nPw+ksx6Ow4xuIuLifXK2HJh63q08AXnnL4OKVBYOrB5mCKxN9L3U/ngQ3qeXvv8TOacO4e/oAO6fb1w956344t0Iec+D0TQCWbz1JuSI5ATAaDbSuW5Ilm575bHdqXJY/914g3mQmJOwpe45fp2KxtFNZm+Lj+OOLgRSt04JC1bUak3/ugrQZPZMuXy6hSK3mZM5uu/mKpVNXjvLPnLqu717N3eM7qfjGp3ZfY8cO7mXbxj9oXrM0w/v15uDu7YwY+KZdWqmR3vvBmj60pxYvE/ASkN/mPf17sNZEwfFPACfh+O6trJk7nYFfzsI9HY5PAAE5cnPu+CGioyKRUnJs3w7yFChsl5azHY8iLh3gwZ5F5OkwGoPrs1HYTAUrEnP/Cua4aK22dv047tlS30f5zv1p982ftP3qD2q9N5HsJSpT693xdpXr3qMIbt4Pp3DebADUqxjM2av3AWhQKZjz10K4FfLMqPfmvTDqVSwIgJeHK1VK5uHctZBU9yGlZNO0j/HLXZDyrXsmLo8M05qx0mzmwJIZlG6adlPbkqROXSGn95I5VzD3Tu7mwrq5VOv7JS52jHgn0G/oaNbtPcOaXSeYMHU2lWrUYdyUH+3WSw5H3A/WpA/60vKzEGIyWs3mv0hKLlChQojaUsodQHdgm96H9kQIUU1KuRetqWo3343oy9lDe4gIC2Vg8yq0fesDVs+ZRnxsLJPe6wpAcOny9Bw+wS79omUqULNxCwZ2aoLBxUhwsdI062if+YcjHY9urphA5LXjxEeFc35qVwJqd+fBnl+R8XFcW6g5EHnlKkaOlwZg9PTBv2o7rvzUDxBkKlQFn0JV7TqG6wc3c3DuRKKfhLLly3745StKwyHT09zug69W89OoTri5GLl6+xFvjdecozo2KsOijcefW3fGsn388FE7Ds3rj0Dwy9pDnLyUeqf7nTOHObd1JVnzFWHh+1qTqnq3gYTdvs6JP7RGQMFqjSnesF1qMn8h5vFD9n03BABpiid31WYElarBho/aYo6PZdf/3gPAv2BpynW3zfnJGTjrfrDZ9UkI4Qfsl1La9/P/D5OCC9RGng0KXAZ66QGtKto8vKfAVqCOlLJmavoFSpSRY+aucXi5s3qkPRfLHpacsH/UKyWclT6oQvEgp+gumW7/fKrUcFb6oBsP0jf4kRyfNLbfCzM1ToSEO0X3tcp57XZ9OsGzZpoRbXDAduPIfwmpuEAl55NwSh8oQAgxDJVSSaH4V2PNtI0WFu/jgXtSygwxsdYKmgshhqOdp2tAz3+2OAqFIjVSDWhCCAOwxllzr/7tSCl/A5yTzlShUDicVEc5pZRm4JgQwr7hLIVCofgbsabJmQM4JYTYj9Y5DoCUspXTSqVQKBR2YE1AG+P0UigUCoUDsCagvSylHGq5QAjxObAthfUVTsDHzfHuTADurtZls7CFgvmse/zHVrL6OGfqyjtD7ZuvlxYzxs5wiu7W30Y7XPNBZOoP1dtLUT/7UxXZgzVPCiT3NKt9D4QpFAqFE0nNl/NdoA9QUAhhOUXaB9jl7IIpFAqFraTW5FwA/AFMAIZZLH8ipfx73UMVCoXCClLz5QwHwoFX/r7iKBQKhf0o1yeFQpFhUAFNoVBkGKyZtqFwAH+H6xPAk8fhTBo5gCvnz4AQDB0/lVLlK6e9oQXxsTGs/OQ1TPGxSJOJAtUaU7lzX/bMncz1Q9swuLiQOSgP9d77DHfvzDZpb/qoNS7uXgiDAWEwUnuElsb7yubfuLp1McJgJLB0TUq0t94g5EnIHf78ejhPwx4ghKBUk06Ub9mdkMtn2DxjDPGxMRiMLtR/+2OyFyljteb6KcMSNUs37UT5lj1Y88X7hN6+CkDM08e4e2em25Tlaer1e7UuPVtXQwKnLt7hrTELGPXOy7xcpySxcSau3HzAW2MWEh4RRYOqRfi0b0vcXI3Expn46OuVbDt4werzAdCmbhm8vTNhMBoxGl2Ys2KLTdsnx40rFxn3wbOkjndvXqNHv6G06/F2urUdVd4XKqAJIfoD7wKHpZRd/859O9v1KYGp44ZTpXZDxn4zh7jYWKKjrc9xn4DR1Y2Wo2bj6umFKT6OlR/3IG/52uQuW52qXQdiMLqwd97/OLJ8JtW6fWCzfvVB03HL5Jv4+cG5g9w7tp06Hy/A6OpGzGPbxpwMRhdq93rmpLRQd1La+fOXVO3ch/wV63Dl4DZ2/vwlHcb9bKWmkTq9hxAYXJLYyKcsGNSevGVr0HzIV4nrbJ/9uVVWczkDstCncx3Kd5pIdEwc8ya8RscmFdi07xwfT1uNyWTms34tGdyrESOnruJh2FM6vP8jdx48pkRwdlZNfYfgl0fbdE4Aps1bha9/Vpu3S4k8BQoxY7kWaEwmE6/WK0PNhi87TN8R5X3Rmpx90CYKJwYzIcTfEtT/DtenpxGPOXZgD807aHm4XN3c8Mlse41PCIGr5/OuTwhBnrI1Ex2EggqX4elDx+RSu7ZtKcHNXsPoqk2cdc9sWxrzpE5K/rqTEkIQq9vaxUZG4O0faINmIIHBmpenm5c3/rmDiXj07HillJzfuY6idZpbpediNODp7qo5RHm4cScknE37zmEymQHYf+IquQK1/9Wxc7e480DLjHv60l3c3Vxxc8IE6PRwZO92cuTNT1CuPP90UZ7jhamhCSFmAAWBlfrD9r+hpRJ/oKcImo2W6y0ELcHjdd2+bz5aHrg/gA+klLa5v1pJel2fAG7fuIavf1YmDu/LxbOnKFqyLP1GjMfTy9tmLbPJxLKhnQi/e52SzV4hqPDzTbWzW5YTXKOZXeXcO6UfQgjy1m5LvjpteXrvOo8uHOXciukYXN0o0WEAvvntMz15fO8W9y9rTkp1Xx/G8jFvsuOnSUhpptPE+XZpht+7RcjlM2QvUjZx2a3TB/HyzYpfzvxpbn87JJwp87ZwfvUoomLi2LT3LJv2nXtunR6tqrJkw18NYdo2LMuxczeJjUvRoydZhBD079kOIQRtX+lJmy49bdo+LbatXUH9l23LqpsajirvC1NDk1K+A9wG6gNfARWB1lLKV4Fvgbl6Msf5wDf6Zl8DX0spK+vbJsu/wfUJwBQfz4XTx2n9Si9mrdiKh6cXC3742i4tg9FIh8lL6fb9JkIunuDR9Wd9OIeXfo/BYKRw7RapKCRPzSEzqTPyF6r0m8LVbYt5eP4w0mwiLvIxNYfNpnj7/hz6YTi2ZlIGzUlpzecDqPv6cNy9MnF83a/U6T2M12dtpk7voWz89mM7NftT941huFs0L89tX2N17czXx5MWdUtRvNVYCjb7BG9Pd7q8VDHx+yG9G2Mymfn1j0PPbVe8YHY+69eSvuNtz6j7w2/rmLtyG1/NXsySeTM5st9xc+HjYmPZs2U9dZqm7wfYEkeV94UJaMmwUkqZ0MFUnWeuTr8AtSyWL9bf/6tdnwACsuckIHtOSpTVMhPXbdaK86ePp7FV6rh7ZyZHycrcOLoTgHNbf+faoe00GPC5XeX1sHAmyl6uHmFXT+PhG0j28vURQuBXoCRCGIiNCLNJ1xQfx5rPB1K07jMnpTNbfk98X7hmM+5dOJGaRLKaqycOoFjdlhSq3iRxudkUz6U9GylSy7onABtUKcLV2494EPaUeJOZFVuOU61MAQC6Nq/My7VK0nPkL89tkyswC79N6s0bo+Zz5dZDm8oNEBCUAwD/rAHUbdyC08cPp7GF9RzYsYlCJUrjl836JnxaOKq8L3JASy0xu+3VAztwpOsTQNaAIAKy5+L6Za02dXjPdvIHF7VZJyrcwvUpJppbx/fim6sA14/s5OiKWTQbOhVXd9vLGx8TRbyFM9GD0/vwyRlM9nJ1eXhOy24ece8aZlPcc4MGaSGlZOO3H+OfuyAVLJyUvP0DuXVSs/K7cXwvvjny2aY5dST+eZ7XBLh+bA9+uQvgk806Z/obd8OoUiofnu5agoH6lQtz7uo9GlcvxqDXGtLhgx+JiolLXD9LJk+WTXmLT6atZs+xK1aXOYGoyKc8jXiS+H7/zs0ULFzcZp2U2LJ2uUObm44s7wvTh5YGu9FcnX4BuqK5pAPsBdqj9bf9q12fEhjw8UQ++/Bt4uLiyJknH8MmfGuzRmRYCFu+HYE0m5BSEly9Kfkq1mNh35cwxcey5lNt6D6wSBnqvDUqDbVnxDx+xMEZgwGQJhO5qjQlsFR1zPFxHPv5U7aN6YIwulKu5yiban+3zxzmrO6kNH+g1myv0W0gDfuMYfvMCYkuVQ36WJ8J6/aZw5zZupJs+YowT9es2W0gBSrV5dyOtRStbV1zE+DAqWss33SMPfM/JN5k5ti5m8xatpvDi4bh7urC6ml9ANh/8ir9Jyzmnc61CM6TjWGvN2XY600BaNl3OiGh1jmWP3oQwtA+2sCQKd5Ek1btqV63kdXlTY3oqEgO797GwNGTHaIHji2vza5P/2WEEFeBSkBfIEJKOVlfnh9tUCAbzw8KFAbmoXl0rgHeklKm6iTrLNen/Jlt79i3hgXH7zhc89ajSIdrAgQHOWU8Jt1N/ZT4L6UPehwbl/ZKdpDZSWmvqhXys8/1KSMhpcyvvx2dZPlVoEEym9wCqkkppRCiC8r1SaH4V/NCBTQ7qAh8K7Sf8DCg9z9bHIVCkRoqoKWC7qReNs0VFQrFv4IXeZRToVBkMFRAUygUGQYV0BQKRYZB9aE5GLOEqHjbnruzhhiT4zUByuR0/HSQOP2Ba0cTFesc3XvhtmcksQZnTK8AqNfZ8bpL5gx3uCbA0ZAwp+imhKqhKRSKDIMKaAqFIsOgAppCocgwqICmUCgyDCqgKRSKDIMa5fybmPvZEE7s3oyPX1Y+mb/+ue/+nP8Dy76dwOQ/DpHJ17Z8apY4ysTil/FDOLlLK+vIeVpZ18yawq6VvyaWr9XbgylVo75NuvGxMawd0wtTXCzSbCJ/1UZU6PgeAKfXLeD0+oUYjC7kKV+byl2t9yqIj41h1ajnTV0qderLgV+ncu3gZoQw4JHFn3p9xtmUhvvPYa1w8fBCCAPC6EK9kXM5s2I6d49uByFwz+xP+V6j8NRzvNlDesxBkjNeaV67JCPeakaxAkHUfu0rDp+5AUDeHP4cXTyM89dCgGeZPWxlxdzv+XPZfBCC/IWLM/DTKbi5e9is46z7IcMEND1jxmopZal/uizJUb15e+p17MGcsYOeW/7o3m3OHtiJf/ac6d6Ho0wsqr3cnrrtezD30+fL2qBzbxq9+pbd5TO6uvHSxzNx9fDCHB/H6lGvkbtcLUyxMVw7uIW2XyzF6OpGVLhtCQ2Nrm60GDU7Uff3T3qQp1xtyrbqReUu/QA4uXYeh5dMp7YN6Y4Aag6agbuPb+LnQk27U7zNuwBc2vQr51fNpGz39E15sMccJCXjlQMnr9FlyE98+1Gnv2xz+dZDqnWdZHc5H9y7w6oFM/luxXbcPTyZOOhNtv+xgkZtbM+s5az7QTU5+XuMUgqXr4pXEpMUgMVff0q794ahZShyHOkxsShc7q+GLo5ACIGrxzPzFWmKBwRnNiyiTOvXE01SPLPYdnMn1TWb4hFCPOfIFBcTBQ5IE+Tq+UzT5CBNe0nOeOXc1XtcuHbfafs0xZuIjYnGFB9PTHQU/oHWJblMirPuhwxTQ9MxCiF+BGqgpf5pDRQFZgBewCWgt5QyVAixFS2xY00045TrwCjABIRLKesIIYzARKAe4A5Mk1J+76jCHtuxAd+A7OQubJ8hSGo42sQCYNvSuexbt4y8xcrQvu8IvOxwlDKbTawc3oXHd69TvEkXAguX4fGda9w7e4hDv36Di5s7lbsNIiDYtoq22WxieYKpS9NXCNRNXfYv/JoL21fi5uVDi1GzbdIUCPZM6QsI8tdtS/462vk8vfw7buxZg6tnJmp+mL6cZ/aag1hjvJKU/Dn92TP/Q55ERDNm+lp2Hb1sU1mzBeWgbc936dW4Im4eHpSvXo8KNerZpJEajrgfMloNrTBa0CmJlu6nPTAXGKoboJxAC1oJ+Eop60opvwQ+AZpKKcsCrfTvX0cLbpWBysCbQogCjihobHQUf8yZRqs33097ZRtxholF7bZdGbNoG8PnrCVL1gCWfjvOLh2DwUibzxfT+bsNhFw6SeiNC5hN8cQ+fULLz+ZTuesHbJnyoc0mKQaDkfaTltJ1xibuX3pm6lLllQF0nb6JQrWac2pdirYQyVJr2EzqfTyP6gO+5sqWJTw4r+W5L9G2D02/WEPuqs24stl2AxNL7DUHSct4JSl3H4RTpMUYqnedzNCvVjDns+74eLvbVNaI8DD2bVnHrHX7mbvpGDFRkWxZtcQmjZRw1P2Q0QLaFSnlUf39ISAYLWht05f9DNSxWP83i/e7gDlCiDfRbOsAmgA9hBBHgX1AVrSg+RyWrk8RYdb1/4TcvMbDOzf5tPvLfNS2FmEhdxnXsyXhD0Os2j41nGFikdk/AIPRiMFgoGarV7h2+li69Ny9M5OjRCVuHt2Fd9Yg8lVuiBCCgEKlEcJA9JNQu3Vzlnhm6pJAoVrNubJvo01anhaGLjnK1yPsyqnnvs9dtRm3D2+2q5wJ2GsOkprxSnLExpl4FK5lEj5y9iaXbz2kcF7bro+je7cTlCsvWfyz4eLqSvVGL3Pm2AGbNFLCUfdDRgtoMRbvTYBvGusnGqXoNncjgTzAUSFEVrSGfD8pZTn9VUBK+WdSEUvXp0y+1vX/5CpUjElrDzJ++U7GL9+Jb0B2RsxZRZas9o+YJeBoEwuA8AfP+mWObVtPzoJFbNaIemxhvhIbze0Te8mSswD5KjXgzqn92n5uX8UcH4eHj5/durdOaKYu4XeuJa5z7eAWfHNaX7mOj4kizsLQ5f7pvfjkCibi3vXEde4e3U6m7Pmt1vxLudNhDpKS8UpKZPP1xmDQ+qXy58pKoTzZbHaTCsiRm3PHDxEdFYmUkmP7dpCnwF9+3+3CUfdDRutDS0o4ECqEqK0na+wObEtuRSFEsJRyH7BPCNESLbCtB94VQmyWUsYJIYoAt6SUqTlGJcvMT/pz/vBeIsJCGdaqOi3fGEjNVp3tP7IUcISJxexR/blwRCvriDbVaf76QM4f2cutC2dAQNbsuXllyHibdaNCH7B9+kjNfMVspkD1puStWBdTfBw7Z3zCsg/bYnRxpXafz2zK8x8ZGsLWac9MXQrqpi5/Th5I+J2rCCHIlC0ntd/6xGrNmMcP2f/dEACkKZ5cVZsRVKoG+6cPIeLuNYQw4Jk1O2W72T/CmR5zkJSMV1rVK83/Brcnm18mlk15i+Pnb9Gq3wxqVQjm47dfIt5kxmQ202/CYkIf2+b9ULRMBWo2bsHATk0wuBgJLlaaZh2723zc4Lz7IcOYpCSdtiGE+BDIBKzg2aDAZTQDlIRBgQ+llAf19ZehNScFsAkYqL//DGipvw8B2kgpw1MqR77iZeRHP610+PEVzOIck5QLoTbH5jQ5dOOJwzUBXAzOaVA4K9vG8PqFnKL7X8q2cTPCOef2neoFMrZJim50Usris2UVpVoy69dL8jm5NpoEPtJfCoXiX05G60NTKBQvMCqgKRSKDIMKaAqFIsOgAppCocgwqICmUCgyDBlmlPPfQmSsiQPXIxyuG53DOdNrcvrY9viLNdQskNnhmgDlg6yfbGsLnm7GtFf6F3FklX2PnaVG+ZeHOlwTYNr3g52imxKqhqZQKDIMKqApFIoMgwpoCoUiw6ACmkKhyDCogKZQKDIMKqApFIoMg5q28TcRHxvDmtE9McfFYjabKFC1MRU6vcfhxd9xbtNSPDJrUxIqvdKfPOXrpKH2jAUTh3B69xYy+WVl2M/rEpdvX/ozO5bNxWh0oUT1+rR6d5jVmt+N/oDD2zeSxT8bXy7REhhGhIfy1dB3Cbl9g4CceXj/ixlkstF3YPangzmuu0l9ulBLK7d8xpcc3bEBIQQ+ftno/clk/AKCbNJNislkolvLugRkz8k3s9OXURYgJjqa7u2aEhsbQ3x8PE2bt6Hf4JEZVve9V+rRq10NhBD8tGwX3y7Yil9mL375vDf5cvpz7fYjug2ZRdgTLZPGh72b0LN1dUxmM4O+WMLGPWfS3IezroUXtoYmhLgqhMiWzPJWQgjr734rMbq68fIns2g7aSltP1/MzWO7uH9ey/paqnl32n6xhLZfLLEpmAFUbdaBtyf99NyyC4f3cHLnBob+tJZhc9dTv8sbNmnWa9mJj6bNf27Zip+mUbpKLb5ZuYvSVWqx4qdpNmkC1GzRgfen/Pzcsmbd3mLM/HWMnvcHZWs1YNWsr23WTcrCn6ZToFDRdOsk4Obuzk+L17Bi416Wb9jDzq0bOXpof4bULRGcg17talC7+ySqdJ7AS3VKEZw3gA97NWbr/nOUbj2WrfvP8WGvJgAUK5idjk0rUKHDOFq99x1fD++UmEgyNZx1LbywAS0lpJQrpZQTHa37F2ei+HiHOAYFl6vyF/ecXb/Pp2HXd3Bx0ybN+vj9JW6nSomK1ciU5XnNA1vXU7dlRwDqtuzIgS3rktkydYqWr4p3EmMVz0w+ie9joiJtSuyYHPfu3GLH5vW06dIjXTqWCCHw9tbcnuLj4oiLi0t3Of+tusUKZGf/iatERcdhMpnZcegireuXpUW9MsxbtQ+Aeav20bK+ZkLTol4ZFq8/TGxcPNduP+TSjQdULpU/zf0461p4IQKaEMJbCLFGCHFMCHFSCJGQGrOfEOKwEOKEEKKYvm5PIcS3+vs5QogZQogdQojzQogW6SmH2Wxi+ZAOzH+zLjnLVEt0Jjq9fiHLBrdj+/SPiYlIMXek1dy/cYXLxw/wv7fbMrVfF66fSV/+f4Dwhw8Sq/9+AUE8fmRb+ubUWDZ9Eh+2rM7e9b/T5i3rDYaTY/LYYQwYPhaDcOylbTKZaNuoOrXKFKBGnQaUrVA5Q+qeunSbWhUK4Z/FG08PV5rVKknu7H4EZvXh7gMtzfndB48J8NeCT66ALNy8+8z/4db9UHIG2u4GlkB6r4UXIqABzYDbUsqyekbbhOrFAyllBWA68GEK2+YH6gLNgRlCiL/YRFuapEQ9Ttncw2Aw0vaLJXSZvpEHF0/y6PoFijfuRMdv1tL28yV4+QWw7xf7U2cnYDaZiHzymPdnLKPVu8OZM6qfzS5Kfyft3h3M5FV7qNa0NZsW/5z2BimwfdM6/LMGUKJ0eQeWTsNoNLJ84x62HDrHiaMHOX/2VNob/Qd1z125x5dzNrB6el9WTnuP4+dvER9vSnmDZGpR6bnU0nstvCgB7QTQSAjxue4vkFANWqb/PYQWuJJjkZTSLKW8gJbCu1jSFSxNUjwzp/28obt3ZrKXqMytY7vw9M2GwWBEGAwUbdCekIsnbT64pPgGZKdMnaYIIchXoizCYOBp+KN0aWbJmo3QEM2EIzTkHpltdPq2hqpNW3PIjqZsAscO7mXbxj9oXrM0w/v15uDu7YwY+KYDSwiZs/hSpXptdm6xzUHqv6T784o91Hj1cxq/PoXQ8KdcvB7C/YdPyJ5Ne0Y3e7bMhDzS0qzfuh9G7uzPrvlcgX7cCUl/K8Pea+GFCGhSyvNARbTANkEIkeCWkeASZSLlEd+kvzd2/f78xfHopOZ4FBn6zKbr2oFN+OVJfx760rUbc+HwHgDu37iMKS4O7yz+6dKsVLcJ21YtBmDbqsVUrtc03eUEuHf9SuL7Yzs2kiNfsN1a/YaOZt3eM6zZdYIJU2dTqUYdxk35Md1lfPQwhMfhYQBER0WxZ8cWChSy3fXqv6Ib4Kf1v+XJ7kfrBmVZtO4ga7adoFvLqgB0a1mV1VuPA7Bm63E6Nq2Am6sL+XJmpVDeAA6cvGpXuR1xLbwQ0zaEEDmBR1LKeUKICKCnDZt3FEL8DBQACgKp21OnQFRoCNu+S3A8khSs3oS8Feuy9dvhPLp6FoTAJyAXNd+03pkI4Ocx/bl0ZB8R4aGMal+Dl3oNoOrLHVk4cSgTX2uGi4srr340yaYO1inD+nD60B6ehD3inaYV6fTOh7Tp9R5fDX2HzSsWki1HLj74wnYD+e9H9uOc7vTzYYtqtH7rfY7v2sLd65cxGAxkzZ6L7kMdn0kivYTcu8fwAW9hMpswm800a9mO+o1fyrC6Cye/gb+vN3HxJgZOXETYkygm/7SBeZ/35rU21blxJ5SuQ2YBcObyXZb+eYQjS0cQbzIzcOIizOa0f/OddS1kGNen1BBCNAUmAWYgDngXWAJUklI+EEJUAiZLKesJIXrqy/sKIeYAoUAlIAj4QEq5OrV9BQSXlK0n/JbaKnZROodzXJ+ckT7oSWycwzVBpQ9yJv+19EGvV82fsV2fUkNKuR7NY9OS/BbfHwTq6e/nAHMs1tslpUyfP71CofhbeCH60BQKxYvBC1FDsxcpZc9/ugwKhcJ6VA1NoVBkGFRAUygUGQYV0BQKRYZB9aE5GIMQeDlhGsCqY3cdrgnQt05+h2s2LpTd4ZoAD57EpL2SHUTFpvJoTzq4Fv7UKbrOoPtH7zhF9713/+cU3ZRQNTSFQpFhUAFNoVBkGFRAUygUGQYV0BQKRYZBBTSFQpFhUAFNoVBkGNS0jb+JJw/usOHr4USGPkAYBCUbd6Jcy+6EXDnL1hljiIuOxCcwF03f/wI3r0xW68ZFPuHMwgk8vX0ZhKBE14+4vmURkfevAxAf9QQXTx+qDrM/E+yq+TPZsHQ+SEnj9l1p2S39SRNv37rB+31eJ+TePYTBwKuvvc7rb/dNty443vXJmbrOOLeO1I2PjWH1qJ6Y4mMxm0wUrNaYip3eS/z++Mo57Jv3Jd1nbk90LkuJfq/Wo2ebakgJpy7e5q0xCxj6ehNa1C2N2WwmJDSCt0bN586Dx1QqmZdvR2iZ8oUQjPthHSu3HE+zvBkuoAkhfIFXpZTfOUCrHvChlDJdXgIABoMLtXoOITC4BLFRT/ltUAfylqvO5u8+odZrg8lVqjKnNy7l8IrZVHu1v9W655dOIWvxapR5fTzm+DhMsdGU7v1p4vcXln2D0dP6AJmUaxfOsmHpfCbNX4OLqxtj+7xKxdoNyZmvoN2aAEajCyPHfk7psuWJePKE5g2rU7tuQ4oUK54uXXjm+hQR8STdWs7Udda5daSu0dWN5qNm4erhhTk+jpWfvEbucrUIKlKWiAd3uXliD5my5UhTJ2dAFvp0qUP5jhOIjolj3sSedGxaga/mbmLs9LUA9OlSh+FvNqP/hEWcunSHmt2/xGQykz1bZvYtHMKa7Scxmcyp7icjNjl9gT5JFwoh/tGkV97+AQQGlwDAzdMbv9wFiXh4n9BbV8hZUkvrlKdcDS7u+dNqzfiop4RdPErO6i0BMLi44ur1zDlHSsm9I5vJXrGx3eW+eeUCRctUwN3TC6OLCyUrVmff5j/s1ksgKHsOSpfVcv9n8vGhUOFi3L1zK926znB9cpaus86tI3X/4lZmik9MFrr35y+o2vUDq93LXIwGPN1dMRoNeHq4cScknCdPn02W9vJ0Q+oJoRNcpwDc3Vys9inIiAFtIhAshDgqhDgghNgihFgAnBBC5BdCJCbtF0J8KIQYrb8vJITYqDtDHRZCPJf/VwhRWQhxRAiRvp9P4PH9W4RcOUP2ImXImrcwV/ZrZr4Xd60n4oH1TwREPbyFWyZfzswbx77PX+PMggmYYqISvw+7dBQ3H3+8AvPYXda8hYpx6tA+Hoc9IiYqkkM7N/Pg7m279ZLjxvWrnDpxlPIVq6Rby1muT87Qdda5dbSu2Wxi6eAO/PJGXXKV1tzKrh3cgpd/IFnzW+d/ejsknCnztnB+zWiurP+UxxFRbNqrJX8e3ac5F9aMpkuzSnyq19YAKpfKx6FFwzj42zD6T1iUZu0MMmZAGwZcklKWAwYDVYARUsoSaWw3H5gmpSwL1ADuJHwhhKgBzABaSykvJ93weden1M1IYqOesvbzAdTuPRw3r0w07PsZx/9YyK+DOhAb/RSji6vVByrNJp7cPE+u2m2pOvRnDG4eXN3wS+L39w5tJKhiI6v1kiNPwcK069WHMW93YWyfruQvUgKji+N6Kp5GRPB2z1cYNW4yPpkzp0vLWa5PztJ11rl1tK7BYKT9pCW8OmMjIZdO8vDaOY4s+5FKnd9Le2MdXx9PWtQtRfGWYyjY7GO8Pd3o8pLWMhn93RoKNx/Nr+sO8k7nZ0bbB05eo2KnidTq/iWDezbC3S3tY8iIAS0p+6WUV1JbQQjhA+SSUi4HkFJGSykj9a+LAz8ALaWU15Pb/nnXp5TNSEzxcfzxxUCK1mlBoepaM9A/d0HajJ5Jly+XUKRWczJnz2v1gbn7BuLuG0CW/CUBCCxXnyc3tF89syme+8e2ElQhfQENoFG7V/nytz8Z99NyfLL4kiNvgXRrAsTFxfF2zy607dCFl1q2Sbees1yfnOkm5axz6wxdd+/M5ChRmWsHtvDk/i2WDu7Awvea8vThPZYN7URk2IMUt21QtShXbz3iQdhT4uPNrNh8nGplny/Toj8O0aZB2b9se+7qPZ5Gx1IyOO2+uhchoFk+IRzP88ec4LGZWifAHSAaSNfPs5SSTdM+xi93Qcq37pm4PDJMM+yVZjMHlsygdNNOVmu6Z86Ku28QT+9dAyD0/EG8c2gXSei5g3gH5cPDLzA9xQYg7KF2oYbcucneTWup/VKbdGtKKRnc/20KFSnGm30GpFsPnOf65CxdcM65daRuUreyWyf2krVAcbrP3MYr09bzyrT1eGcNot3ni/DyzZaizo27oVQpnQ9PD60FUr9KEc5duUtwnoDEdZrXLcX5q5pVYr6c/hiN2q2aN7sfRfIFcu1O2laMGW6UE3gC+KTw3T0gUAiRFYgAWgDrpJSPhRA3hRBtpJQrhBDuQMIgQhjwOvCnEOKplHKrPYW6c+Yw57auJGu+Iix8vy0A1bsNJOz2dU78sQCAgtUaU7xhO5t0i3Z8n1M/j0Ga4vDImpMS3UZoB3poI0HpGAyw5ItBb/AkPBQXF1fe+mg8mTL7plvzwL7dLFu0gGIlStGsrtZ3NmTkWBo0bpZu7f8Szji3jtSNDA1h2zTdrUxqbmX5Kta1WefAyWss33SMPfMHEx9v5ti5m8xatpufx71G4XyBmKXk+p1H9B+vTYepUa4gH/ZsRFy8CbOUDJi4mIdhaWcvyZCuT/ogQBkgCrhnOe1CCNEf6A9cAW4BV6WUo4UQhYHvgWxozlAdgbzo0zaEEHmBP4DeUsp9Ke07qFAp2XnyYocf0+mbYQ7XBOekD6qYO30eoCnhrPRBzuK/lD5o7bmHTtH9ZaJjarJJiT78zYvj+iSlfDWV774Bvklm+QWgQZLFl4Gt+vfXgZKOK6VCoXA0L0IfmkKheEFQAU2hUGQYVEBTKBQZBhXQFApFhkEFNIVCkWHIkNM2/kmEECHANStXzwakPL3afv5Luv+lsv7XdP9LZbVVN5+UMiDpQhXQ/kGEEAeTm0vzIun+l8r6X9P9L5XVUbqqyalQKDIMKqApFIoMgwpo/yw/KN3/VFn/a7r/pbI6RFf1oSkUigyDqqEpFIoMgwpoCoUiw6ACmiJNhHBwgn4nIoSVjh2KDMl/5kJV2IajbmwhRDFglBDCfi+85HWNFu9TSshpq6aQeqewEMKxBgBJ9vNv1vuvkPS4HfHDqQJaBiHpxSEdMNqjX2C5gQDgfSGEV3o1dV0j0EgIUU9PuPmaECLdufksgll3YIwQIkt6NS0RQhRI2I8jgpCFhov+Od1Wi5blcmTNOkFXCJFFCJG6o7D1JP5ICiF6AkPSK6gC2t+MxYVRRLfGc3eEpsXN3FUI8bEQon1SKz4bNQ1SSrOUciNwFCgF9HVQUBNAZmASWvbgtVLKeEfcgEKIakAbYKCUMjy9QcLi/1UYWCuEGAHpD2oJ/zMhRBXgjBAiSEppSk95k1wHPYBhQohXhBDpTiGsl7UV8CewXr/G7NYVQuQDFgohKuuLXNESqqYLFdD+ZiwujMXAO2gXR8X0akJievE30dKLjwTstnySUpp1zYFoAUICdYAhQgjvdJY3HtgPxAK7gWJCCM+EfdpCkhqJO1APKAy0029wU3oCj/7/agGM1svcSeherukJavq2DYGX0M7tViFEdr28dt2XFtfBm8DbenlnAi3t0bNECFEU7dp6G+gJVCYZQ28biAJ2AB8JIcqgnYP0dz1IKdXrb3wBBYB1gB/QFjgB+NupVdDifRZgKlrt5y1gPZrRixvgZYOmsHgfBGwCvPXPTYDv0PxOrdZMZh9B+l934BVd8xV9WQkgux1lzQv46u/fAaag+aj+ZV0by+oLHABqoVUAygC7gOHpvA6KAleB6mhN+knAdYtzY7BD0wD4o01QzQ10BzYARnv+R2iBS+haK9BqZ1769/mBC0BnW8to8T4bMBBYDvymn4NKaMHy5YRzYctL1dD+fsLRboh30foMWkspHwkhGtjSjySE8ERrAo3WFz1Gu/h26ZpNpZQmtIu6nJWaBplw9wvRFO1CzoF2MwNs1svfBa35aXPtRAjRF5gnhJgEdJJSLgQOAjWEEL8CC9F+rdPEoqwDgVnAr0KIKcAC4AZQWwjRyXJdOzChZYC4KrUa5Cld/3UhRHr8954Cm6WUe4AHUsrBwDFgixAiQEpptub8Wq4jtS6CR8BF4EegK9BEarW+IUKI+jaUrxBa7dlfSnkTWKYvbyqE8JdSXkWr/XlaK6jXmBNq/q8AuYBFwBagIpoxUVNgKNq94WZDeTXS8yujXlb9IiU8jeGu/4PcgJ+AI0AJ/bs6aDdKcSs1c+t/i6DdBCP1z23QTF3a65+76boFbSxzdWAjWoDshVaDqmGh+TkQaMe56IkWcPPrF/IxYIj+XQ3gI6CkjZpt0GoOBmAysEVfnlnXmwBksvZ/ZfH/ygm46+9HAXsBT/3zS/o5WJnwP7ThOnDT//rox9/bYp3OaDXiPwAPG89DP2Ci/r4PWnOzmv65g76vIjZq+gAz0GujaHaOP+vnuTVwCWhox3XwDnAy4bpEq6G+Dfxu67X6F+30bKxeVv8DW6FV2b9Hc44qo1+04/Wb5RTQwgodgVZN/xHw05cVBE6jNQN99At7s35xHLUmQKAFxiz6+5bAfZ4FxVJoHffn9P1etPXG0HUqAe3Rmtp90ZrddfVAYXfzDa3PrCkwTA9srhbldsWO5jzQDNgDzAXmA/mAT4Az+nm+CNTUz0cpG3SbojWvZqA1tfOhecWOQOsm2AHUBr7Ftm6CD9B+KEpZXCejgTn6ed4NlLby+rJsxrugNf2+QxtkAa3WtwutSd9IX2ZV81jXzwVsT7iGeP4H5CO0GrqntZp/2Ye9F5J6WX2xFUWrNXW2CAzF0frSeqFVrWtb/nOt0PTQL/w++udg/WYboH/21/cRYMUF5g58jR4g9eX7gD0Wn41ozvEvAwXsOAfv6jdyIbRf4xVANv27ZcCqhM9plTeZ8tcDbgIrLZa/ASxF7/uzQjcQeBXw0m+48/r5LaL/f3ah/Vh0QWvCl0SrxR4G8lq5j2poPzxt0foir+rnJRdaLfI7oDTaQM5+UgnESYJOVn3bQLQft95ogbgkWndBCayoTaPXRvX39fXjrG/x+Xugv/65J1owb27tOda3MwDewFr0WjPPaqw59WOxqz85cR/p2Vi90vwHltFv1tEWy14HzgI1bdQyJPncAq0W0Vv/HAwcB8bboGnU/7oCVdBqBgk1nIPA7w44B630cuXTP+dAq0HW0G+aedYEsySafYEv9ZsqK1qt6TRaDWi4vj+rm65otY5ZQA89qEy3POfANKCrxfqV0WqWZa08v356QPvc4rsAtGZXWYtltdA62lOs9SUJZj3Qmm+/o3X+r0Trf/oNmG3D8fuh1eKK64HwGpp37QqeNTfroTU3B+mfP0IbhLK2OV+LZz/AC4AlFt/1RPths6mZnex+0iugXqn+EzOhVaFXo3WwJ1Sv30Eb0fLHxhEo/WbKg1Y7qQBsA97QvyuiBzlbA0ROtIC4Tb8hEoLadrSO6/Scg3eAj/T3CbqD9POyO62gYFG+hNG199D69wqi9Qt9ZaH5GVptpZgd5fwAmI426nYb6GXx3diEG1n/nI9URmLR+gjz6+8b6oHmTWBfkvWm8Kxv0qj/P63qQ0Jrwi+2+NyaZyOkTdBqxFYFG32bEWg/BF8ADfRlFdCC5TCLYyljsY1fKnoJ17pBP7a30Gp5ndFqwov162sScMhSN13XmyNE1Osv/8RSQFn9H+mOVguZCuSyWDe3lZoVeVZj6In2q74C+AWtBlgOrSP5PX0dVys0awBd9Pfv6YHhSz3I7ENrZiUEnz+tLWsK+3oJrb+wqMWyFvqxeFqxfS793L2NVpP8EK12MwhYg9b8NlqU157pDk3QprnsQKs9TEEbJf0IaIfWF1nPSq38aDXwUkAxYHbCzWpxfkuiNS1PAlVsvcb0a+sYWv9epiTfD9C/S7PPTF/fRf/ra3HcCbV+F/362gB8bLGN1T/C6E1ytCD2GlrN71V9WVu0QZ1CDrsHHSX0or94Fsxaof3i/IT2i1SBZyObs2wNDmg1u51oo4Kz9QuvAFqfz0q0+UJN0Jq2WazUbI42+XYMWjOioK7RD632sxn4yp7gkMy+MqPVnMbrgawb2rwuqy5i/QbuiRZwe6P9OGwDfrW4Gfui9UcZsLIf0kI/UD+/xfXP76HVyMagBbfPsGLAxqKsHdD6JMsCYWgDCJa1va/162AT0NyWayvJsu5ofXu1edY0NgKfYuVouYVWG7T+wJxoI6Sn0ZvsPKs5VrS1rLreNeAl/bM3z+ZI9kj4/zny5VCxF/Gl/5MSOjjLo1Wjs6ENAFxAm+RYBa2mtgAbpyXournQqv5nLZYFovXttE0oh42ajdFqCPP1z+5ofShfotVK5mHH1IwU9pUDrem5Fq2WYlXzgmc/Ej3Qmu3r9EDzgGf9MT31G7CwnWXzQ2um19E/u6L1zf2J1kwUlmWxQs8XuIv2g9EWrUY5GahrsY4r4GOLrr5uV7TRyz5oPxSvoNWeamFFzTwFzXJoNdBiFsvmowV5m5qBSYLZB2ij2l3Rpig1sfhus36d+Tri+rJ8qYm16UB/+Hki0EKf6BqB1gdTDq163Q0tUIwHqqJ1LJ+yQtdg8d5NSnkL7SJ+LIT4EUBKeZ9nzQ+ASFvKLqXcgNZv8rIQorOUMkZKeQZtJDJMStlN30e6kVLekVLOQKsJvCalPG7ldlII0RWt5vgRWm3HhDaKN1AIMR0t6HSQUl6ws2yhaP059YQQpaSUcWid6g+B7VK/AxP+WsFjtBvYBa3GuBDtMZ+mQogGulaclPKJLbpCiPfQzkMo2sj5ev2VMC/MXrekGLSAVlcI8YkQYjNgRgvMi4QNmVASjkUI0RhtEOSAlHI+Wj/Z/4QQzfXHyB4Dk6WUYXaWOdVCqFc6Xmh9FrPQfo099GXDedZP8C5af5dNzQB9295oI49j0Ubf8qDVHLah1Vq2YUcHeJJ9tEB7KHgMWlP0CBD8T59Xi/KNBQbr793QfjAWofWhZcPKZnYa+8iN1lRbD4xDmxdYL52a+dCbm0B2tM72SaTSkZ5k+4RmZEINcQYW/W1oAX6m/v49rJw+ksx+MqE12Xeh1cxLof14NrBWE621kNBE7YlWY16VZJ0OaDXhzThoACDZsvzTF+x/9cXzz6S9rQetdmjNiV5ojwj1Rvv1q2GHfle0JmFjtP63L9A62HPpN9xG0tFZn2RfbdBqPr+ij879W1562VZg0VRHGx0dD2R24H580PoRP8CieZhOzfJo8wPfQmt22zMhuYh+Ta1Gf6pCX14GG6ZmWLGfhPlglfSA1MCGbQuj/dD+jPYYU3e0fuT+SdbLgo1dIzYfhzPFM+rL4lczt8Wyjmh9Dwl9Wm+gDQq8bKs2Wkfs5+hzn9D6eT7g2WhnTiCng4+pLvpcsX/TC63pMw6tc74BWi1yJVY+wP5Pv9BGqa9Y++PD8yPQ/dBqeZP0c3CTZyOQXdFqO77YOBCSwn6Neln3YfFQvw3bT0ZrSr6rf35J/yHq93ee73Qn1XsRkVJKIUQzYLIQ4jAQh3bxxQNthRCuaCOSc6SW5ysxT1VyWH6v/zUJIa4DrYUQ26WUN/S+szVCiLxSyutOOKZtjtZ0BFLKMCHENLTa70i0czxISnn3ny2ZdUgpDwkhyki9z8wK/IAJQssUnBttsnATtEGAjcBnQsvGWx8t00WYg8ppEkKcRQumV9K6ZpNhBtp0kQ+EEI+klL8JIe4D3wkhHkgtCYHTUQHNDvT8ZS+h9V3cR+s3WIHWH5UVbZLjNinlPUi74zfheyFEe7Qmxny0KnwutGyuS9A66wVg7Y2RYZBS3ga+FUL8hFYbifiny2QjVpdXSrlGCBEL/A/YK6W8JISYjTZiCNoI6g9oT588dGQhpZRP0WqTaV6zyWx7EbgohAgDxul/PdBy3u11ZDlTQ/lyWolFhlE/tCwDp6SUtfUMoxJtqP8PKeUSa2tRSTKMdkOb0Lobreo/EK3DthpaUysGrQ/lqOOPTvFvQwjRGu2a6i+l/FUf+e6J9sP2haNqZs5Ab71MQkuR9Lq0YmTfYftWAS1tLILZS2gjOpfQ5kQNkFLO0tf5ArgnpfzSFk39fXa0JsQBKeVFPb9XY7Rn/7brQdQkpXzs8INT/GsRQjRHe3B9vEVQ87ah+fqPIYQIRKvohfyd+1VNTivQg1kVtBG3X6WUO/VfoU1CiFJos/Qboz0HmSZJgllf4H0gGq1DtreUcooQwgRMEkIMllJud/xRKf7t6M1PM/CDECJeSrmE/0iXg3TQHEZbUTW0FBBCFEQbup6p56pfhJbMr7DFOjXQZmpvBt6SUt4RQhillinWmn3UQpuAOwFtjtlbwEUp5Sj9+7fRmrEOHwRQ/HfQJ6peklKm20Qko6MCWgoIIXKhBZnLUsr7Qoi8aI8f7ZVSvmuxXiW0h6SHSSl/smZ0SG86FETLiHAGbZKsGa3v7D3gjtRSMisUChtQjz4lg17LuoX2EPVWIcREvZbUEggWQnydsK6U8iDQCc2M1zcVzaS53y+izXbPgTYsH4fW5PwB8BNCZHP8kSkUGRtVQ0uCxQBAMNpsfy+0KRlLpZTjhBC50Z71Oyal7COEcNHnmnlIKaOt0H8NbQb5fZ6ld/4ULbvF72hTM9ys0VIoFM+jBgWSoAezlmgz06+ijWh+BHwlhDBJKScKIbqg1ayQmsckaNMqUkUI8Q7a41DT0Zqzq9CeMBiDln0gTkq5Bm2AQKFQ2IgKaEkQmvP2J2ijlo3RmoBRaCORP+g1ss/QEuElkly/WTIDBPmAoVLKLfr3N4DPpJQdhRDj0J7dVCgUdqL60P7KTbRsA+XQMmmURZvc2l3/vMMaEX3uWAn9/ctCiJxotbquFqttAWL05uoSKeU1Rx2EQvEiogJaEqSUN6WUB9Ae1p6vd97PQctBdUhKuc2ygz8VcgPdhBBzgSn64zsDgXJCiMn6OtXRam3eDj4MheKFRDU5U+YE8LbQ3MxbomUNuA6pP+eWMKggpTyhP8/WH80zMuFB61bACiHEL2i55bs7+pk8heJFRY1ypoAQIjNa0sZWwCwp5Vobt6+F9oxnEJqf5XZgnT6nzRct/5iL1DKmKhQKB6ACWhpYTMuwOp2KECIALWutB1q/WyO0dNwr0JyAsqGlwIlPSUOhUNiOCmhpYEdeqITtKqPl8PJCC2510QJbNTSDj2MOLahCoVABzZEIIXqh2bON0D9XQBvVjAfGSSkfCyEy/QfzeSkU/wnUKGc6SGa0cyvaQMJwACnlYbT8/42BEfq8NBXMFAonoUY57SSZFECl0EZGmwOrhRBmKeXnaOletgBfWZuFQ6FQ2IdqcqYTIUQfoDNa0/I4mjHKfjT7ua1ATaCplPLcP1VGheJFQdXQ0oE+taMC0AXtmcwDaBNlA9FSAkWiPep08x8rpELxAqFqaOlET/5YDO1pgPp6rrOHaNkzJkopY//RAioULxCqhpZOpJQxQohIwEUIkeBuvgbNwk4FM4Xib0TV0ByAXksbiDbPLAjoJKU8+48WSqF4AVEBzUEIzVw4O2DWs90qFIq/GRXQFApFhkFNrFUoFBkGFdAUCkWGQQU0hUKRYVABTaFQZBhUQFP8pxFC1BNCrNbftxJCDEtlXV/9UTVb9zFaCPGhtcuTrDNHCNHBhn3lF0Iosxw7UQFN8a9ECGG0dRsp5Uop5cRUVvFFM8BRZFBUQFP8reg1kLNCiJ+FEMeFEEuEEF76d1eFEJ8IIXYCHYUQTYQQe4QQh4UQi4UQmfT1mukaO9GSaCZo9xRCfKu/DxJCLBdCHNNfNYCJaM73R4UQk/T1BgshDuhlGWOhNUIIcU4IsRHNICet43pT1zkmhFiacEw6jYQQO4QQ54UQLfT1jUKISRb7fju951ahAprin6Eo8IOUsgzwmOdrTdFSylrARmAk0EhKWQE4CHwghPAAfkQzrqmNNpk5Ob4Btkkpy6IlEDiFZlZzSUpZTko5WAjRBCgMVEGzLawohKgjhKiIlnCgPFrArGzFMS2TUlbW93cGeN3iu/xoGYubAzP0Y3gdCJdSVtb13xRCFLBiP4pUUM9yKv4Jbkgpd+nv56E5YyVY+/2m/62G5mu6S8+j6QbsQUsEcEVKeQFACDEPeCuZfTRAy3iCnocuXPdKtaSJ/jqif86EFuB8gOVSykh9HyutOKZSQojP0Jq1mYD1Ft8tklKagQtCiMv6MTQBylj0r2XR933ein0pUkAFNMU/QdLHUyw/P9X/CmCDlPIVyxWFEOWS2d5eBDBBSvl9kn0MtGMfc4A2UspjQoieQD2L75I7XoFmjWgZ+BBC5LdxvwoLVJNT8U+QVwhRXX//CrAzmXX2AjWFEIUAhBBeQogiwFmggBAi2GL75NgEvKtva9Rz1z1Bq30lsB7obdE3l0sIEYhmOdhWCOEphPBBa96mhQ9wR3+mt2uS7zoKIQx6mQsC5/R9v6uvjxCiiBBCGU6nExXQFP8EZ4DXhBDHAX9getIVpJQhQE9gob7eXqCYlDIarYm5Rh8UuJbCPgYA9YUQJ4BDQEnd0HmXEOKkEGKSlPJPYAGwR19vCeCje0H8BhwFlgI7rDimj4F9wAa0oGvJOWAb8Afwjn4MM4HTwGF9msb3qBZTulEPpyv+VvQm1WopZal/uiyKjIeqoSkUigyDqqEpFIoMg6qhKRSKDIMKaAqFIsOgAppCocgwqICmUCgyDCqgKRSKDIMKaAqFIsPwf3V/J6wlK1prAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 23;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\nfrom torchmetrics import ConfusionMatrix\\nimport matplotlib\\nfrom mlxtend.plotting import plot_confusion_matrix\\n\\n\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\n\\nfor x, y in test_dataloader:\\n\\n    with torch.no_grad():\\n        pred = lightning_model(x)\\n    cmat(pred, y)\\n\\ncmat_tensor = cmat.compute()\\ncmat = cmat_tensor.numpy()\\n\\nfig, ax = plot_confusion_matrix(\\n    conf_mat=cmat,\\n    class_names=class_dict.values(),\\n    norm_colormap=matplotlib.colors.LogNorm()  \\n    # normed colormaps highlight the off-diagonals \\n    # for high-accuracy models better\\n)\\n\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\nfrom torchmetrics import ConfusionMatrix\\nimport matplotlib\\nfrom mlxtend.plotting import plot_confusion_matrix\\n\\n\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\n\\nfor x, y in test_dataloader:\\n\\n    with torch.no_grad():\\n        pred = lightning_model(x)\\n    cmat(pred, y)\\n\\ncmat_tensor = cmat.compute()\\ncmat = cmat_tensor.numpy()\\n\\nfig, ax = plot_confusion_matrix(\\n    conf_mat=cmat,\\n    class_names=class_dict.values(),\\n    norm_colormap=matplotlib.colors.LogNorm()\\n    # normed colormaps highlight the off-diagonals\\n    # for high-accuracy models better\\n)\\n\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\n",
    "from torchmetrics import ConfusionMatrix\n",
    "import matplotlib\n",
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "\n",
    "\n",
    "cmat = ConfusionMatrix(num_classes=len(class_dict))\n",
    "\n",
    "for x, y in test_dataloader:\n",
    "\n",
    "    with torch.no_grad():\n",
    "        pred = lightning_model(x)\n",
    "    cmat(pred, y)\n",
    "\n",
    "cmat_tensor = cmat.compute()\n",
    "cmat = cmat_tensor.numpy()\n",
    "\n",
    "fig, ax = plot_confusion_matrix(\n",
    "    conf_mat=cmat,\n",
    "    class_names=class_dict.values(),\n",
    "    norm_colormap=matplotlib.colors.LogNorm()  \n",
    "    # normed colormaps highlight the off-diagonals \n",
    "    # for high-accuracy models better\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fb26fdd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pytorch_lightning: 1.5.1\n",
      "torchvision      : 0.11.2\n",
      "pandas           : 1.4.1\n",
      "sys              : 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:59:51) \n",
      "[GCC 9.4.0]\n",
      "numpy            : 1.22.0\n",
      "torch            : 1.10.1\n",
      "matplotlib       : 3.3.4\n",
      "torchmetrics     : 0.6.2\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 24;\n",
       "                var nbb_unformatted_code = \"%watermark --iversions\";\n",
       "                var nbb_formatted_code = \"%watermark --iversions\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%watermark --iversions"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
